{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb9548f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import rdkit.Chem as Chem\n",
    "from rdkit.Chem import Draw\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6bb1e3",
   "metadata": {},
   "source": [
    "## Load data and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68567215",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "BASE = '../data/'\n",
    "ignore_chiral = False\n",
    "\n",
    "data = pd.read_csv('../data/real/acs.csv')\n",
    "pred = pd.read_csv('../output/uspto/swin_base_char_aux_1m680k/prediction_acs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6d7faf",
   "metadata": {},
   "source": [
    "## Display the error cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efe72594",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "2\n",
      "real/acs/3.png\n",
      "gold: *NC(=O)CCC(=O)N*\n",
      "post: *N(C)C(=O)CCC(=O)N(*)C\n",
      "grph: *N(C)C(=O)CCC(=O)N(*)C\n",
      "pred: CN([R])C(CCC(N([R])C)=O)=O\n",
      "(643, 1125, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pnqWkqaGBJpsN3d0CoQmBUdMw+/hgtljU/alOSkqJ0+m87n0nTdMuucr+sp+H7OzsUillxJd6sXJFVzp24irPqe+x0ha81vlLSvka8BpAZmam3LNnj7eK8qVIKamvryc7O5vDhw6x7tNP2bF3L1VNTSAEfgYDaZGRZI0YwagZM+jVpw+JCQn4+PioL3cnoes6FWVlLFu4kAtlZehCXHF8pJQ6QaERhEeEYxYQHBxMv4wMQkNDm6/Kb5QQ4syt3AbFpbGxkZLiYoxCECtpjsy6rlNVVUVNTQ3h4eH4+qpOYErra43AfB5IaPE43r2sU6mvr2f9+vX8/ve/Jycnh+jISNIHDHAFXgAhqKyp4d0VK/ho1SqmTJnCAw88wJAhQ/D39/d28ZVbQEpJVVkZC998ky1Hj2I0GjH6+KBf4f6T5tSx223U22wkJSfzX//1X8yZM4fw8HBA1cS8yRN8d+/ciZ/RSCyfR2aHw8GZM2c4ceIEWVlZ+Koc2Z2S53La6XCgadpNXzDfaq0RmHcD3YUQKbgC8v3Ag63wPl7T0NDAnj17ePXVVzly8CDh4eHc9+CD3D5zJiEhIa5akxDknDrF6+++y6EtW1i8aBE1NTX4+fnRv39/zGazOhl3dFIinU4a6upoqq8nNjaWpPR0rOHhyJZjJIVAq62lvLSU4xcucOLECebOnUtERATTpk3DarV6bxsUnE4n+fn5/PVPfyLWYuG27+rNcy3X19ezZs0aPvnkE373u98RFxfn5dIqram+vh6LxYLJZPJqOW55YJZSOoQQ3wFW4hou9aaUMudWv4+32BsbyTlwgL+98grbt29nWN++fO2JJ5g4cyYRERHNX2gpJVGxsSTGxbF90yZeeflltmzZQmpqKoGBgaSnp3t5S5RbQgjXFbbRSGx6Og8/+SSDhw69LHmBcDiwOxys3byBN956naNHjvDmm28ydOhQ4uLi1EWaF3n6CTQ2NNB0heftdjsNDQ04HI42L5vSdux2O3PnzmVCO5jWs1XuMUsplwHLWuNve9v5M2dYt3Qpe7ZvJy4mhq9/97tMnjqVwJAQ4NImST+LhX7p6YQFBHC2oIC33nqLjRs3MmDAANLS0jCqnLudhhQCo9VKeFQUycnJV15HSgIig6h2lPOXX7/E+XPnqa6uJjY2tm0Lq1zG04lT6pfPHuR5TunE3Mf46OHDDB4wAClbP+3mtahpH2/SqbNn2bJnDw02G9OnT2fE2LEEhoRcdk/C81jTNKKjo5kyZQqxsbGcLyhg386dFJ/vdLfduzb3mEdPL17P8W/5o2mCmPBYBvcditVixeF0UF1dfUO9upXW03x8DAaEu8Xri8+3RVIJxbt0Xafg6FGaKiq8XRQVmG9WfWMjlfX1+AUGMmToUMLCw695ZSWEwGQyER0dTVx8PNXV1ezbtYszJ060YamV9kBKcNodNFU2IJ06mqZhUUPpFKX9kPK6M0+1BRWYb0JDQwPl5eU0NDRgMpkICQ3FbDbf0GsDAgLo3asXBrOZvIICLpSUtHJplTbVosXEc8+y5Y/NZqO2tpY9W7ey9MMPqaupITAwkJCQEAwGgwrOitIeOBxo7SAwq5ucN6GiooKTJ09SUVGB0Wi8qebHgIAA0tPTCQoKwmaz0aTuWXU6jY2NnD9/niNHjmC325uDrRCCispKdu7YwYqlSzm0fz+h4eHcfffdhLhvgyiK0g60k++iCsw3wVPrsdlsN91xy2g0EhAQgNlsxmaztVIJFW9x2O0cPnSIf7z6KsGBgTh1vTngakJQ39DA2bNnKSwsJDY2lqeffpp7773XK+n+FEW5CiHaRXBWgfkm+Pv7ExUVhb+//7Xn8ryCxsZGioqKqK+vb6XSKd6kO51UXrzI2epqKk0m7LpOud1Ona4jpaRbZCQzpkylV2YmkRHhjMjKIjIqyuuJDBRFaX9UYL4JQUFBJCUlERgYSGVl5U01ZddVVXFi/36qysqICA/HTw2V6lQ0g4Hk1FSmjR1L77Q0bA4H5U1NFFVUsGXzZsrOnaO6tpb4+AQGDx5IREREc4YhRVGUllR0uAlGoxF/f38sFgsNDQ2cPn2a2tpa/P39r3mClVJSVVXF0ePHMZlMZPbvT0pKShuWXGltRqOR5LQ0Js2ezagRI5CAU9epq68no18/Xv/Xv1ixfh21Bg18LUwKDW1ORqMoitKS6pV9E4QQREVGktatG/X19SxZsoRTp05ds1lbSklDbS0njhzhXEkJUXFxjLrtNjUJe2cjBAaDAR+LBb/AQAKCgggOCSE2NpZ77rmHmbNmgaaxfMUK3nz3XS5cuHDJuGdFURQPFZhvUnp6OnfOmkVaUhLZ2dmsWrWK8+fPNyeJ8JxopZRIXaeutpYD+/ax4IMPuHjxIqlpafTs2VPlR+5srhFkAwMDGT16NIMGDkRvaODA5s2sXL5c9TdQFOWKVGC+SX7+/vTr14/JI0ZgdDr529/+xttvv83RAweoraxEd49btdvtlBQVsX7lSv71xhus3rGD5KQkHrr/fgYOHOjtzVDaiOcWx9BhQ7njrlnEJERSWHiBefPmceTIERwOh6o1K4pyCXWP+SZpmkZ8cjIPfutbnK+tZcnSpbzy8svsXrOGex54gPSBAxGaRlV1FTvXb2DRhx9yqqyMbsnJPDh7NiOGDCEwMNDbm6G0ISEEVouVmXOmcaHyLG++8g6HDx/mlb+/wm+e+xUJsYlowoBAdQRTFEUF5i9FMxjo3qMHP37mGWLj4tixfTv5Fy7wh+efb26CcAJOA/gGWhnVZzSPP/4Eo0eMICQsTPXE7WRutL4bFRjH5KypHNmRy7Jly9i2YysrtnzGPdPvJzQgHBWXFcW72ku/DxWYvyRN0+jWrRtPP/00d911F7t372bT0qUUnTjhut/o60vSkL7MvGsGaTE9SUlMwc/fz9vFVm4xT18CybW/1EIIDJqBzIGZPHD//ezft4+zBWf55wtv0CMug6yhw7GoOboVxasMJhO0gwlLVGD+CoxGI3FxccTGxpKUlMToYcNoqK52nZyNJvwjQkhIisfHYL0kPaPSSQiByWwmKi6OJF0nKjISi8VyzZf4WCwMGTKEB+67j4WLFuFokKzYso3kbmmkqOkfFcVrhBD4hodDO+iYqwLzl/TFABscHExwcPANr690fJqmER4dzTefeYbSmhrCIyNJS0u76vpCCKSUREdH8/jXv87QrCycuk5gZCRB/v5tWHJFUb5ICEFkVBQWX19vF0UF5ltBBd2uy9ffn2Hjxt3Ua4wmE8nJySQnJ7dKmRRFuUlCYDQauev++0lOTfX6OV0F5q/Ac0+xvr4eh82G1dcXs49Pc81I13UaGhpwOp34+fmp6f06mS9zLNXxV5T2yaBpZGRk4O/n/b5AKjB/RXV1dSxdupT8w4eZOmMGfTMzMZnNSCm5ePEi69ato6SkhPvvv5+oqChvF1dpQy3nZTYajSo3tqK0UwJACCLCw71dFEAlGPnK6uvrWb16Ne/+85/kHjqE0+EAXCflsrIyVq1axYcffkhZWZmXS6q0NbvdTnZ2Np988gm5ubnY7XZvF0lRlA5A1ZhvAV3Xcbqn97vScyq7U9dkt9vZvn07G5cvJ8hkIjkxEcxmbxdLUbos3emkrKSEiqoqdCAsLIzw8PArphDQdZ2amhqKiooIDw8nJCQErY2GUqka8y3gmVP3i82ULZerJsyuR9d1amtrqSopwVlfD+riTFG8Std1Si5c4GhODkeOHqWkpOSqlabmWQGPHqWsrAxd19usnCowK0prkxKhgrKieF1TYyOL3n+fH37nO/z4Rz9i6dKlVw3MdrudPXv28OMf/5h169bR1NTUZuVUTdmK0hZUi4mieJ2UkpqqKkpKStD8/amuqrpmjbmxsZHS0lLq6ura9HakqjEriqIoXYau6+ju4ax11dXYmpqumu/eMyS2rfsIqcCsKIqidBlC09CAxoYGtq9Zw7H9+6//mjZu8VKBWVEURelSBOArBBUXL3Lg8GHXxEPeLlQL1w3MQogEIcR6IcQRIUSOEOJ77uWhQojVQog89/8h7uVCCPGyEOKEEOKgEGJQa2+EorRX7enLriiKixACi6bRUFdH7unT7W44643UmB3AD6WUvYHhwLeFEL2BnwBrpZTdgbXuxwDTgO7un6eAV295qRWlA3BN9aihmUzIdjCVnKIoLkYfH3r370+33r05cOAAekcLzFLKQinlXvfvNcBRIA64A3jHvdo7wGz373cA70qXHUCwECLmVhdcUdo7TdOIiIggrkcPfEJD28U8r4rS1UlAM5mIT0khNjGRM2fONI9Rbi8155saLiWESAYGAjuBKCllofupIsCTCDoOONviZefcywpbLEMI8RSuGjWJiYk3W25FaffMZjPjx4+nd58+JCclYVJZvxTF+6REM5tJ7NkTvaGBVRs34nQ4MGgaOJ0YjN4fRXzDl/BCCH/gE+D7Usrqls9J12XGTV1qSClfk1JmSikzIyIibualitIhGAwGUlNTyRo+nNjYWAwGg7eLpNyEoiJ48kk/nn/+W+Tl/Zv//d+B5OZ6u1Rtq6gI7r8fUlNh8GCYPp3OsQ+kRDqdpMTFEeTri8PhcE02YzLygx98Hso++6wXDQ3PtHnxbigwCyFMuILyPCnlf9yLiz1N1O7/S9zLzwMJLV4e716mKJ2S0+GgsrycoqIiiktKaGhoQErZnIrVM6uUZzpQm81GSUkJpaWlKo96OyUl3HknjBpl54c/fJXu3R/g8cfzKC72dsnajmcfjBsHJ09Cdjb8/vd0in0gpUSXkvjYWEKDg5vnOzCbJQsXQmmpd8t3I72yBTAXOCql/GuLpxYDj7p/fxRY1GL5I+7e2cOBqhZN3orS6dTV1LB+6VLeffNNPpw/nxMnTlxz/cLCQubPn8+iRYuora1VgbkdWp8zBpMJHn/c1rwsNbWW0aO9WKg2tn49mEzwzW9+vqx/fzrFPvBcOA8aM4bMkSORgMPhQNOcPPGEgxde8G75bqQxfSTwMHBICLHfvexnwB+Aj4QQTwJngHvdzy0DpgMngHrg8VtZYEVpbyrLyvj3a6/x2fbthMfHExwURN++fa+4rpSS06dP85e//IXIyEgmTJhAQEBAG5dYuRpPGolDBb3IyLCj63qXmYBGSondbqepqQmTycihQz4MHizginMvdQJSoglBWloamhBIXUfXJU8+0ciIkSa++13vFe26gVlKuYWrH5mJV1hfAt/+iuVSlI7Dnd5PulP8Hdy5k4IRI0hMSwMuzxrkSfGnX2WqUMU7BC2OlZTU1tY235bozCSAlDidTiorKykqKiI4LBCnHoeUxubaZWcjAYRg+NChaJqGw+lE6jqCah5+2J9//MN7ncDU+A1F+aqEACEwaRqyro61y5bx6aefUlZefo2XqKlA2xUhkMDI0ototbX0Cc3m+Hs5FPzhD9TU1LhX6bzHy263c/78eQ7+5CdEDRtGQnwKfZ57iOzlneCG8tW4L5B77d+Pr9NJEGBy2Dn/3HN873uSd94x0NTkneCsArOi3AISCAwKolfPnuhGIyvXreNEXp6qFXcQAritspLvnilA6DCx5zp03cS+fzgIX7UKm83GqVP+bN7s7ZLeek1NTRzOOcLxX/6SUe++S1Rjo2t/lH1MU14B/3xsW/Nn+OBBOv4+aHGB5fPJJ4T+7GdouFtMgIzXX8fynzeYM8fJxo2pXimiCsyKcgtIKQmOjmb4xIkk9+jB3r172bByJedPn0aq4NxueW4p2O12vltSglVKKAJRCQu5k41yAi/v/YiTJxfxxhupREZ2nmMppcThcHCxtIyVa9bQ94MPsDidzc8L4FM5m3Ufl5OaCn36SH76U4iO9l6Zb4kWx8//d79DNDRc8rTZ4cD8f//Hd79ro6bGp61LB6jArCi3jGYwEBIeTtagQQRbLMx//33+8+GHVFRUeLtoyjVUVVaSk5NDjMPhWlAGHIJYCvmI+zhNGlbrEMaP/wtBQSXX/Fsdia7rHDt2jBdfeIGPP/yQaLv9snViKeSjhln85pVlbNtZx2efQffuXihsK9HOXzqStxZXR0xTYSGRkZI33/wQq/WPbV+uNn/HLqCzXFF/GS3nL710LtPOv08cDgdWX19m3H03YydPJr+oiAWffcbOXbtoamrydvGUFjyfy9LSUrbv2EHBypWf93B9GXj/83XLfXyIDgnh8MGDrFu/nhMnTlBfX99hv+dSSioqKjh06BBr1qxhZ/YezCYT+lXuoZeYTKxetYpdO3dSXFyMs0WtuqPT/f2vvFwICnNz0dxpdNv6WHs/91gnIXDlRm5oaqK0vByz2XzJUIuO+iW+UZ6eneC6Eq+srKSurg6/AF8CA4IwGoxA5+zd6eHpvRodF8eMmTM5cOAAh3Ny2L51K0MyM/Hx8U6zmHJluq5z7tw5sue9z/8tXgyAU9MwuPMmg+tzHeJwMDksjM8OH+bjBQswaBqBgYFYrVYvlfyrkVJSXFzMpk2bWLhwIXmnTrG3pgajlEghEC3OVRIIsts5vGgRK0wmgvz8CA4K6hRZ7CZs3oxWU4Pk0mFHTsAgJcmjRnHid7/zStlUjfkWcUpJaVU1c99+m689/DC/+MUv2LdvHw0NDV2jB647k05FRSUHDhzgd7/7HQ899BB/ePE5so9sp662GmQXuECREpPJxNAhQ5g4bBghJhML581j2UcfUV1Z6e3iKbiO0bnzF1izZi1bX3+dn3/yH3yAlVlZnP75z3HGxyOFQCYm4rzjDjSnk5f27+fRuDjOnTrFZ0uWsHz5ckpKSponP+gobM4mjuUdYenSpSxbtpyq0lKyq6uJq6qicdAg7K+/jkxMRAqBIy6OkiFD8AE2nT1LwabNLFmyhK3btmOz2a77Xu3Z/zQ0MGnLFrBa4fnnm7e5LiyM/4uP5+2AAIwNDUz54Q+Z4YVtVTXmL8nTFGaz2XA6nRQ1NfGPt95CGgyUl5Vy7uxZdu3aRWFhIVFRUTQ0NOB0OjEYDJ0uSDudThoaGjh1poA33nid/dnZFF64QE1tLRcunmfvhu3cPmEa9zz5JAEhIfhZrWid9GJF4hpWExEVxSNPP0098O777zN/8WLie/ZkxMiR3i5imygqgu9/H3bvhuBgiIqCF1+EHj28V6bm76zdzqnTp8j5+CO+8+abmKTks9Fj0b7zbeKmTcXwf//X/BojYPvHPzB95zv8YtcuGiMjWVRbi8Fspk+fPvj6+uLr69ucdrW9klLidDqoqavi8NGDbNywgf27d7OttoZ4m426QYOw7tqFZjDAk08CrlqjVlrGgcceo/+yz3h7z24elzoWPz8yMvoRGBCAyWSCDvZdnrx3LxMbG7EbjVSvX0/Y0KGIH/wAgFM7d3LsN7/hpU2baBo2jG/s2sV79fXsWLny0hRorUwF5i/BkyGnuLiYtWvWcPrkSXz9/IiOjWHmzJmkpaXR0NDApk2b+M+CBZSVlPDee+9hNBpJTU3FarV2igDtdDqpr6+noKCARYsXs37TZs6fLaBvz5488dhjREVHk5+fz5qVq3j3449Zn5NDv+HDuX/6dJLj4/H19e20rQmawUByjx7ceffdHMvNZefu3SxbvpzuPXq0uynmbjVPjuVHH4X5813LDhxw5Vj2ZmAGqKqqYu++fSz785/53YoVmICVkyYx/MMPCQoIwHiFmYVM3/oWemIi2uzZ/L6khF5BwfxiwwaaGhuZMXMmEyZMIDo6ul3nx9IdDs6dOsX67at57Z23KSooZFdTPYk2G45hw7Bs2YK4wrSkYWGh+H38MUX//d9Ez32Dt7Ozua2omNDQcAaldydjyBAsfn5e2KIvx/TrXzPx4EHqgb99/es8npJyyfPdMzIYPXkyG3ft4q8VFezo25e/HTrE8DVrcN59Nyxb1ibTt6rAfJMcDgcNDQ3k5uby4UcfsnHdehouXuRrd9/N9Lvvpn///gQFBSGlJCMjg5TYWNYvX86Kzz6juKSEOXfeydChQ4mNjcVkMnXIoCSlxOG0U1RYzObNm1m3dh2btm7BFBzMfV/7GlPGjaNv375YLBZqamoYMHAgixcv5qP//If9hw5xeu9e7r3rLrJGjCAkNNR11d0Jmc1mBg0ezOSRI8k5cIBly5bRs2dPoqKirv/iDuxqOZa9Rdd16uvruVBYyP59+9j3/vv8fuVKTMDaqVPx+9mzBLqD8pW+j0IIDDNn0rhpE+bx43kkL5eE3n14rqiIhQsX0tDUROagQXRLSSEwMLBdfad1KSm6eJH8U6fYs2E9W3dswawb2V5ZT3RdA3X9+2PeuPGKFySei2Yfiw+mP/6Bk3W1pM6fz5rCC3xvwcccT0qk2majT79+REZGtv/Kxs9+hvn557EZjYzy9WVqSMhlq5jc39k+vXpx8PBhVpjN9LJaOWw2E7hypevKcu9eCAxs1aKqwHwDpDtdXUNDA7l5eWzcuInNmzZy4tQphg4dxpDevZk4eRKJaWmYzObmD2dqt248/MgjDOrblzWbN7Nm5Ur+VlBA/8GDmTl9OgMGDSIkOLjDBGgpJbrTSXVtJYdyD7Lo4yVs3rCF6poaxo4dy8QZMxg1bBhRoaHNX/Tg4GAyMzOJiYkhrXsP9u7fz+oVKzhfUMDO3buZOWsWfXv1wmqxYGyx7zoLP39/pt9+OznHj7Nk9WoWLVrE4MGDsdlsnW5bPQ4fdk0R6G0SCdJ1MV1WVkZOTg7HFyzg/5YscQXlmTOJ/+Mf6ZaWhvkGLg4tI0ZAbi56RgYTjuRgKg7jfh8fjBYrUkoC/P2b8557+9h6WmMcdjunz51jx86dbFy1mtwjR1hTV0d0bS3OrGH4btmK0K7dkcugaYSHhRH07ruUhoQQ/uqrvLhuLbMjIwmKjcXX3x8/Pz/8/f3bb6ewn/4U/vAH8PXlr9OmcX7duiuupmka6enp9E5PZ8eOHZTbbPj6+vL6T3/K95cuxbBlC8THw/btyN69gc87fcKlx10IooEXgSFAJVAMfF9KrjtxpgrM1+Bpsq6rq+PkyZNs276DPfv2cvTwYcJDQrj/3nu5/4EHSUyIb75abHlgNIOBsJgYRk2bRu/BgxnQpw/rtm1n37795B0/zqDMTKZMmUpaWiohwcFYLJbm7vntiZSSJrudyspKigsKWL9mGRv27+DM6Qv0yejF4P6ZzJh6O91SU12TjXPpB9RoNJKUlMTDX3uIKVOmkN49jb3Z2axZs5aTp04zfOBABg8cQJ/BgwkMDLxqzaWj8WxDj/79ue+xx8gvKuLAgQOcOnWK6upq4uLivFzCrkCSfy6fJQuXcvCdd3j90EFMQPY3vsGAP/6RkMDAm/vOJSUh8vORffowurCQjWFhjN2ymV07d1BRVsY3n36awFauTd2opsZG8vPz+cfrr7N6xQrCTSY2NDURXVuLnpWFtnnzTTXLGo1GQl95hTp/f/z+/GcWXbzIuL/9jby8PO6+5x5GjxlDREREK27Rl/TMM/CnP4GfH3U7d1L5l79cc3Vfi4WsjAx2JiSw99Qp10KjkYbly/H/+c9dnSX694d//5vG22+noaGBoKAgtEuDsgAWAu9Iyf3uZf2BKFCB+UuRSKSuU11TzdEjx9i1cxcbN27gcG4uSd27c/f99zNj8mTS0tLwsViAK18he5YZDAbCo6O582tfY+SUKWTv2cOqFStYs3o1u/ftp1fPnkyeMJ5RI0e6DnA7Cc6erEi1tbXsyclh5erVnNq7lzN5efQaNIj/+cGDjBs/hqjwGAzi6sHUs9xkMhEXG8N/fetblJaWsmHDBlYsX85HH85n9aqVjLvtNsaOG0daWhrBwcHt9+r7JglNY9z48RTk5fGnP/2JvNxc9GvcX/ZcECIEpg54kdL7e5NY4PMcjgHHMDzyiFfK7+mYWd9US97JXOq3bOKNw4cwAktuu42Qb30LX3dfj5slQkLQT57E1q8faSdPsrOujtsSE8nPz+fcuXOkpqZiMBi8doEppaShoYGLxcXkHD5MSWEhAWYzK8+fJ7qujsZBgzBv2oS4yW1vnlv8l7/k2Llz9Pz3v9lQVcU3Cwo4eOgQiUlJWCwWrFbrFZvG24KUkqamJmx2OwZNw/Lzn6O98ALC3x9yciA4GE9WhSt9A4UQGI1GYhISiE9O5sCZM81/F4AXXoCBA+Hxx+Hee+H73+f8178OgL+vr6v103X+Hg/YpeSfn5eNAze6He0mMDc1NYGuY75GoGsLuq7TZGuisrac3Xt2seSTZRzcm41Bl9xxxx1kjR/P8EGDiAkNvalyemrTUZGRTLntNnr26EHKZ5/x4ZIlLPpsKQWnT2EyGsnKysJqtXq9eVtKSWNjI6UlJezbv5+Pli5ly/bthFksTL/rLmbMmMGAAQNcY3MFiBvo+tLyQiUqKorZs2fTvXt3Nm7YwGeffsqH78/j5MlTjB03llGjRhEZGYmPj4/X94PD4aCpyYZE4mM2X35s3EPFPL9ficlgIHPQIMZlZVG+ciUV1dVXfU+Hw8G5c+ewOxxER0VhtVguuUXS3k1kLc82Pccb39jNU5oGX/saBw9CdbW4ZC7fK3V+u9Y23sj6LdcpKy0nr/AI2/71Cr/8dBVGYNXEiYz84AMCg4IwfoULP2GxYDp6lKaJE4nfvJmt+fncvnYtK9PSmD17NsHBwYSEhLTpzEyebXc6nZw7d46d27fz3nvvcSY/n40lJcTX1eEYNgzzli03HZRb8vX1Je2ddygPDyf0lVf4544dTHbXLDVNIyUlheDgYKDtzuPNTfcOB4VFxZSUlZH6ysv4vvMO+PvD0aOuJuiqKnRdR+fzXvpfZLZYGDJuHONPn2bDnj2ui+SWHnkE+vaF0aOxvPgi/itXkvOvf5GWnEx4VBRGkwkQfYHsL7s97SIw2+12dmzZQlNVFcMmTMCvxb2Klge2OZOUrjcPS7neMIWWU+xda31XhyYndbW1bNq0ie37t7Blyxaqy6sZlTWE24ZPZNLs2Vi+Yk9iIQQGo5FuaWl8/amnSO3fn+zdu1m7dCl/+9vfyM3NpV+/fgwaNIiAgIA27bXc8sNdV9/Ahg3r2bJxI1u3bsUBPHDHbDIHDWTSpEmXNNfdSFBuybM9Pj4+DBw4kF7p6QxIT2f1uvWs27aN/Qf2s2fPHsaMHcvoMWMI9Pdv89qH5zPT2NjIwYMH2XfgICajkWFDhpDeM/3SZCHuq2yT0XjFGphwz1zUd8gQvvatb3GxqYl169ZhvkqwbWxsZNPmzezOzqZHUjID+/VlwNCh+HWQeZtdbXh38n37i/z58WH4/B5SUlwtgC1JKXE6HDjd303jVfZfSw6nE6fDcdV9B2BzOLDZbCxespgj897h+a3bMQK7v/EQ8d/8f67bJV+xNUYIgTCZEGvXUnnvvQR/+ilrTp7kgblzOXr0KFOmTGHGjBltfmFpa2qisLiE1+bOZceWLRSdO8fW2lqiampoGjwY4+bNaF+hNiuEQEqJ0WjE8txznGtsJP7111lTUsJ9H33EiRMnueuee5g0aSK+FkubtXpJKWmy2Th/7hzzPviA/u+9x9ATJ9B9fdGOHQP3bSMhBL6+vgRbrVit1iu2TgohsFitDBs+nFmzZrFmzRosFsulx3HQIJynT+PMyCDl6FEsU6aw7JlnmPTww8TExQFfLZlQuwjMNpuNxYsXs2/7du4oKGBwZib9+vW74r2a0osXKS4qorq2lsjISJKSkjCbzVf/23Y7xcUlnDtbQExMDImJiZ9/WKRsbs5oaGxk8969HN6zhwXz51NeXs7gwYOY/vWpjJs4lgi/aMy3KHOT5wD7Wa1MGTWKicOGMaRPH15//XVeeuklkpOTufuuu5gwYQIpKSmYzOYbrpV+WZ6gXFtdzf4dO9i5/wALFi1COh306tWLKdOmMW3qNHytluZe1LfihCOEwOrnx+jbbmPQ6NFkrlvP+jVr2LhhA9u27+DI0WP079eXkSNHNvd4be0Tnae1IDc3lx07drJixXIO5OUxKmsEfXr3uiypREBQEFPvvJOBNTVkZGRc8fMohMBgMDBo8GD+3//7f/To0YPQ0NAr9uI1Go2EBAdTUljIrg0bWB0ezsw77mD0iBGtut23kifPtNQFZ1fkExUVdcnFjJQSh91OWWkp1TU1mMxmIiIimjtPXYnD6aSsvJzKigoS3MPtWvxBJK4WrwslJZzIy+Pcpwt5fosrKK+6dzbdf/NzYgMSbulFnsloxPrhhzT86EdYX36ZD48c4fHaWjb4+JCWlkb3tDSsvr4ITbTa99fz3bXZbJw9fZqd2XvZunUr1aWlrCstJaq+HsewYWgbN2K4BSMgPMHZ6udH2F//SqGvLzEvvcSHx44xp7GJjZGRBAUFMiAjg+Dg4Fb/znpuW5zJz2fHzp30mzuXOwoKaDKbObVoEb1iY5vX1YxGuqWnk5WVRUpKylVjh9FoJDk5mfHjx9PQ0EBcXNzlFxmhoeSuXIlz4kQGlJXx4G9+w/GcHGK3bGE1vV/8X35rQ2TtRsoPbnabRHsYS5mRkSGfffZZVqxYwf79+4mNjWXixInMuesukhITmw+qruusXrWKlStWcDwvj6ysLJ544gmio6OveOWj6zoXiopYsWIliz9dyKxZs3jwwQebv9BSSuw2GwVnzrBm/Xo+WLyYiwUF9ElPZ/z48YweNZq0HmlYLRb33fzW+2I1NTWxe/duNmzazO49e8g7dpSsYcMYPWEMYyePIiEqGaNmarUy1NfXc+TIETatX8/azz6joraO9Ix+TJ0yhSFDhhAdHY3Vam31L1hjYxMXi4vYtm0bi1asZM/Bg4T7+3HnrFmMHTuWvn37No9/bg0Oh4P8/HzWr1/PurVrObZ3L8ndu9Nn1CgmT5zIkL59L9sPTqeT+tpaHLqOxWLB4uNz1aQLnvvHtbW1aJpGQEDAZa04Ukrq6uo4duwYe/fuZc2yZZw4eoQZM2/nt3/9a7aUMrNVNv5WEaL5pFJitbLmjTeYMGECkZGRl+QerqyoYNeOHRzLyyMoKIhRo0aRmpp61f1WU1vH7uw9HNq/n7vuuovY2Njmk6WnJa2hsZFPVq9m2x//yCs7dmAENj74IN3//EfXeUJc3hL3VXjOn7quU/zLXxLz3HPowLdCQ4l46ikeeeQRElOSm2vOrfH91XUdXdcpKylh0Qcf8Na8eZy5cIHspiZiqqqwDx2Kcds2uIVJUFrGjaamJqr+93+JfP55bEIwJSISv6FD+OXPf958odpawdkzYqaouJgFHy8g6rnfcn9pKXVGIzs/+IARM2ZgafF9dTqdFF+4QFlFBWEREUSGh2O4woWap8WsvLycCxcuEBMTQ3h4+CVxxvM9Xb58OdZf/IIZx44BrhYjCQxnB4/xtu1b/PNxpPxACDKAICm57sSZ7aLGbDKZmDhpEgkJCcz/8ENO5xewbPlyMvoPICkx8ZJ1z547x45duzh8+DAVFRX06tWLGTNmYHHfm25J13WqKio4eiSHTZs20atXr8sSsJeWlvLpokXM//BD/ENCGTt6NHfcfjuDBw8muI2GMgkh8PHxYfDgwXRL606//v354L13yc3L48iJoxRcPMn4gWMYMGAIASEht7Q8TqezOZH/8qVL2b9rF4HBwdw2fRpTp0+nZ8+eBN5sz9UvSQiB1WohLiGBqdOnE56QQMiyZZw+eJBP5s8n73gu4ydOICsri8TExFuebam4uJgDBw6wadMm1q5bgzA6GTywHzNn382g0aMJCw/HeoWmSYPBQEBQ0A1vo9lsJtTdR+Fq6/j5+dGvXz+SU1KICwtl4X8+xSfk6q9pjyRwRgjefustiouLueeee4iPj29+vrq6mu07d7Jh40YiIyOJiooiJSXlis2fuq5TVl7Ozh07WbViOWPGjCEmJqb5eafTSUlREZs3b2bP3//eHJS3PvYY0c88Q3hYBJpmuOWB0fNZ0DQN/x//mBwfH3r/8pf8q7ycP779Nn8tL2P6XVMYmTWK0ICIVgnM9fX1HDp8mLWrV7Nh1SrMJjP7nU4iq6qwDxkCmzbd0qAMl17YmM1m/H/1KwqdTmJefJFVpReZevgwr776KjNnzmTUqFGEh4e3SrO2w+Fgy5YtLF+xgqx332V2aSlNZjOr33iDIaNHX9bKqWkaYRERBIaEYDKZXJnOrrJ9mqYRHByM1Wq96q0Tq9XKsGHDOPbSS9TefjsB7vSdzbdzeNH8R55554zgWSAf+P6NbFe7CMxCCMJCQ8nMzCQuPp4zBWdZsWI5vtbLg63NZqO2tpb6+nr279/P+++/T0JCAv3797/iznPY7TQ2NFBbW0tjY+NlN/sbGhspLSsjJaUbjz3+OL179yIqMrLVa4df5Ln3YbVamTx+HN1TktmzZw+f/OcTli9YxY7Fm5k5526GjBtLUmIikV+yc0XL7b9QVET2nj3s3rWLjZs2YTGZGZGZyeSZMxk4bBjhERFe6VlqMBgICgpi5NCh9EhN5ezJEyx47132nzrGjj/tZMLYCYwfP4FBgwaRkPD5if5myunZD1JKqqqqyMnJYfPmzSxfvpyamhpS0lK4/e4pjBiQRVx0En4BgQitbT8PPj4++Pj4MG7ybaT3zUDz8eHnP//fNivDlyWFwBEVhSwvJ7O+nm/s3MnatDRqamsvWc9mt1NUVMTp06epq6trzj19pRO4lJKmxkaKi13rNzQ0XPJZ1nWdqqoqates4a9bt2IAPpk2jW7f/S6xsbGuc0Mr3gpyXVRaCXnySfaGR9D/O9/mmaIi5i1eTN6gFPr06U2gJRiT8dZ14vP0n6mtraXgzBkOHTrEubNnWXXxIpH19dgyMzFu2/aV7infCE3T8LVasf/iF5xoaiLt1VdZfuYMj+3eTW73HvTq1ZvAwMDL79N+RZ7Mg6dPn2bUBx9we1ERdZrGmr/+lV6jRxMaEnLZxbsrYYrlhu4ACyEwmUxXTYDkCd6eIWL+X8ip7bmdAxiQss/NbFu7CMzweU0iKTGRmOhoUpISCbpCLcTTJBIcHIymaWzfvp25c+fy05/+lKSkpMt7QV7ngxATE8OTTz6J0+kkyd3d35u9Xz21pd69e5OQkEDv3n3YsX07Gzdu4M3332Pxpo2MGzOGWRMnkpKSgtXdLH+9MrdscquqquLosWOsWruW1cuXY7fZ6NW3L/ffdx/9evYkPDKyuZObtwghsPr4kBATQ0x4OPFxsew9kc0nHyxi+/btbNmyhclTpzJt6jS6JSddMib4mp0B3UknpJQ0NjRQcOYMq9au5ZMFC6itrSUuLo45c+YwevRo0tK7EeAfhObluV78AgNJaydjY2+EdDgQuk7h4cNEjh7N3TU1hC5fQeWjj16ynt1up6y8nHL3z6effsr06dOvOBZW6jqNDfVUlZdTUlKCzWa7JDDX1tZy9K23ePTNNzEAm554gt7/8z+kJCe32axeRqOR6Kgogh99hGNxsXS/+24eKirms2d+x192HuaRBx8ja+KkW/Z+UkrKysr4+9//zvKlS6k+f54NjY3Euu8pGzZv/kq9r2+KEAQGBWH48585GxBAwp/+xDtHjjDnb39jT3Y2jz/+GOPHj8fvFqbvPH78OB9//DE9X3qJ2ysqqDeb2PbyS4yePYeg8PDmnAqtzWKxEBcXhzM2FuOFC1dapeBm/2a7Ccwtmc1mEhISrvq8ruukpKQQExnJzu3b2bRxI8OGDeOee+7B/yrza16Jp5bavR3O/C2EICAggIEDB9C9exojR49iwccLWL95M/PefIvDO3Zw55w5jBw9msiIiOam5qvdnwOoq6vj9OnTbNu+nQ8+/JCSsjL69ejB5AkTGDNuHEnJyVe8JeBNQghMZjOJKalExseQkTaYTRs38+mnn7Jo4ULWrV7NtGnTuO+++0hISMDf3/+qQ1Sae53rNgrOnmXXtl2sWbWaHXv2EBERwaxZs5g6dSo9evQgMDCw04yjbmueWkpIair5q1aTNHkSE87kU/HHP8LChZet7+ljUVBQwIkTJ/D397/ylIotOnh98Ribdu5k9gsvIIDFc+4i+XvfJzo6GrPZ3GZ5ATwd/Cw+PkRlZbHhrbfI+vrXmVlVS8KCxSyJScYUEUlCQgJRX+GWlJQSu8PBwYMHOXr0CNn79uFrsbCooYHY2loaBw/GuGkTWhu2dnkqTBaLBZ9nnqFA10n8y19YWFTIgzkHWLN2LWazDxn9M4j+CilpPS0EJ0+eZNu2bQx5/XWmVVRQazKy5MXfMGzsWPwDAzG00aQiLYeANvzyl2jf+x5aY2PLVeqBn93s322XgflGGE0m+qWnY2xqYtPevcyfP5/MzEz69OnT7md6uVGeISTBwcH07dOH4MBABmVm8tmyZezavo2z589ztqiYSePHkRgbi39gYPM9E0/PSXCdyC5evMjOnTtZumgRhw8fxmYyMXH6dO6bOZOeaWmEhoZi8FJSgBshhMBq9iMluRuBAUF0796dhQsXsn7DBpYsXcqZM2eYOXMmI0ZmER4ejslo9ryweWyx1HWqKyrIPX+MjxZ8wuaV27DZbfTq3YtvPP006WlpRLt7D3eGz483eS4se2YNJ2f5ctJmzCDk00/hiSfgzTeb1/F8V51OJxcuXCAnJ4e0tLTLW66EaO5Qd1lHoi1b8L/9dtB1Fs2ZQ/SP/od+fft4LVGPwWAgMiKCrJkzWfPGG4z41rfoX1ND8Asv8HFEBCMnTiTU3/+mRze0HKdcU1vL5i1b2LJpE3lHj7Lh4kXiamtpGDAAy44dCC99l00GA1Ghodife47jlRWkvzGXecdP8KBzKauNRgICAwkLDUXTtJs+T3tyCpSWlrJlyxZ6/PnPTD5/nlqjkXm/+gWTbp9NSnx3r7VwWZ96Cvz84Nln4cwZiaum/LMv0yu7/Z6Jr8Nms9EzI4Oeffqw98RJ9uzZw2uvvcZPfvITYlt0j+8sTCYTKd26ER0bS7++fdizezeffvop7735JqsWfMyUSZOY9cADRMfG4uPjg6ZpON29CvNPn2bBggWsXbsGi+4ks19/Zj/+OKnp6cRGRNxQnuD2QghBeHg4wcHBJCUlMXPWHSz97DMWL1zIwYMHGTtlFDNun8bA9MEEB4WiGQyu+8h1dZSXlLDo7XdYvW0D+SUlZPTuz5w5c+jduw/pPXtiMrbzJPwdVI/hw3EeOwYZGfDWW66Fc+cCrpNtQEAAoaGh1NfX89FHHzFgwADCwsJu7Fhs2gQTJoCu43jzTSbdc881xzi3pYCAAKbPmcPxXr1w3nUXSfn5PPzDH/LTBx+k6sEHycrKIvAGOw161NfXc/DQIbbu2Mkbr/0LR10dW2pqiK6pwTl8OKaNG6EdtPQYTSZSXvkbZd1iCPvZb/n3yZPMev99Cksucvc9d9O7R4+bbqlsqK/n2JEj/GfRIvq8+CKT6+qoM5vJ+eQT7h0xgsCgwFbtR3BDHnrI9cNXuzrosIFZ6jpmq5XRo0dTY3fw5z/8nmWffca4ceOYMmXKTTVpdwSeE43VYqF7Whrx8XH0z8hg3eIlbFy3lnc++IDdR44wecoUhg4dSmRUFEUlF3ntX/9i7+5dlFeWMzArg6/dfx+9EvoQl9INg8no7Y/xTfPsB6PRSFxcHFHR0aQkJzFi6FCWLF3CkoWfsX3tRmaMncS9jz5BbEI8JeXlvLd4CdvXrObU/v2kpffk/333B4wZM6Z5HLyrdtXR9kbHYDKZMMXGQm6ua3YeT3D+0Y8A8PPzIzoqipKiIoqKisjNzaVPnz7X7++xcSNMnQq6jnjvPUwPPUR7usTUNA2LxUJ8YiLH582j7PHHycjN5ZV//5u/4Mqg1X/AAAICAy/Js3wlntS4eSdOsHbtWrZnZ+Pn48Pic+eIrqnBNmQI5q1b22RKwhshhMDs44Plh//DyfI6Uv/yAouLi3ly714+8/GhauQIgoODXS1115mVSkentraGE8fzWLd+PaPffpvb6uqoNZv57IUXGONOxmQ0dNhwdpkOvSW6rhPg78/EESPIHjiQhWvX8tprr5GUlMSAAQPaxVXzreZpxvO1+tKrd29iwsMZN3UKixcs4NNPPuHAwYPEJSSQnJTE+QuF7Nu7l769e/HIIw8zctwo0rqlYjKYEUJzXV120F3k2Q8mIUhMSCAyIoLYuFgaGhvYuH4Dr8/7N8dO5TOoVy/25eWxce9eKouKGNStG488cD/T7rqreQyx5+8prSw09JLgHF1ZCUYj/v7+pCQnI+x2Tp07R05ODlOmTLlmf4egvXsxPvMM6DrMmwcPPNB223ETPC08ISEhrHvlb3z2nW8zIy+Pn7z/Ph8GBRGfmIifv3/zXMhX6xvhdDopLy9n/759LFu+nENHjpBjsxHnSbPpHqfcnggh8DcHkfqn5ykwmEn84x954/AhphWcwWFrIiMjo7k/wbX6heg4qagqZ9/+ffT961+5rbiYapOJJX/+M5PvuovIyMhO9/1tX0fyS9A0jcTkZObc/wBh4REcOnSI+fPnc+rUqU47ET18fn8uJDKSXv368cDDD/Oj//05t02bwfkLhSxdupSCM/k89OAD/OjHP+bOOXfRo1s6ZqMF4bm30wk+y579YLVaSU5OJj4unj59+zLhttu4WFnBivXrOJyTw6RxE0hKSiY6JISU+Hj8/fya73F1ti91u+YJzmFhBC9cyDd378ap6/hZrfRKTcVhs7Fz504uXLhwWc4Bj3G6zsAf/9gVlP/973YblFvShCC9ZzqOP/+Fj4ZnYQYe+vvfyXvxJQrOnKGupuayjHIeno5x69etZ/HChTTW1nLIZiOxro76AQPQ169vd0H5i6w/+AEHv/UtTMCK6mr03bv55JNPOHz4MNU1VUh55W1vrKuj4NwpVm1YQbdf/Zqp7qC85l//YtDkSdfMFNeRdegaMwBCYPH3ZeiE8Tz+9NP8/aUX+fDDD+nbrx+xcXGd+qQrhEAYDAhNIzk9nbhu3Rg+fhwDB2RQVFhIfFwcI0eNIioqqjn1XGffH7quExsby6OPPkpDQwNFRUUgBH379aPw7BlkQ0PzPujM+6JdCw2F48dxpqUxPj+fXwYHs33cOOLi4li0ejU5OTkcO3aM5ORkAgMD+MEPXFPpAlzcNoKh9mQQv8bx/vuY7rvPq5tyo4SmkZCQQGhYGEttdn73m1/xk0OHuO3ll8iuq8X23/9NYrdul+Ti13Udp9PJ4cOlfO/7Zg4dmIGtcTBZeiE2/bs0ZAZh2b691ccp3woREREYf/tbdtvtDHnjDd44coRvCsG28HB8/EykpqTia/FD0wxIoLBQ8v++L9mx3YzRFE5EUU/erbdSaTLx6g9+yNN33U1IgH/z/IqdzQ0fUSGEAdgDnJdSzhRCpADzgTBcs2g8LKW0CSF8gHeBwUAZcJ+UMv+Wl/zSwhEWFsZjDz7Ake3bWL5hAx9//DEpSUmuTiTt/Gryq/IM09A0jYS4OO6/7z50XUfTtCvOE91ZNU9yIiUhISGMHj26uRZSUVHRnCqvM7ekdBihoZxctoyYceOYU1lJyqZNlP/hD4QuWED+6dO8/fbb9OnTBx+fXvznP/A//wP+27czLu8ktfhz+Be/oOc993h7K26ar9XKtCmTONW9Gy//6td859NPGTx3LhsPHkTOnUu3Hj0wm82u/M92OxdLLnLPPUaE9i6RQS+ySVZzsS6Vc73Gk7zl5bYbp3wLBAUF0fP55ylMSiLm5z/nnzk5zHnuOfLLT/DQfQ/SK6kvfn4BSAlz7oQ5d9czpt9LdH/+z0TVJ3HSGE/xf/7Ko4MGE+TvGg/dWc9qNxOxvgccbfH4j8ALUso0oAJ40r38SaDCvfwF93qtyhN4wsLDeeiBB+iRlsaenTv5+N13OXXsmNfmBm1rnmZdk8mEj4+PK+VcJxk6drM8Q83MLadpVAG5XdGDg/nvadOoNBgYuH8/A199ldmz7yQ8LJwjR45w6tQpjEb4xjfgpe+fIfU73wFggdAoHTvWy6W/eZ7zlJ+vHykpKfT+5jf53R130CgEY3fvpvHhhzl8+DC1tbU4HA5OnDzFr3+zhcrKEnytc9lYW0tsXR29BpsYvf8VjNeYvKc98Wy3pmn4+fnh/73vc/Jnz2IEFpaWUvnpKub9800O7NyJlJJVq+w49Eac5jfp9dKL3FZZRbLPcar/8QDpQ4cSFhbq7izXec9rNxSYhRDxwAzgDfdjAUwAFrhXeQeY7f79Dvdj3M9PFG0UGcwWC4PGjWPy9OlYrFZWLV/OpvXrabx0wLeiKO1EvdnM1JQUGqxWQhct4r927yY+OoqS4mKOHTuGlJL/6rGa+R8ZqdID2NItleMduAXMc8EYFBjE0CFDGDBnDj+bNo1KTWPggQNEPPggx44c4cyZM2Tv3cuGDRfBuZtl+WeJq6rBNiQTbetWTCYzWgdrCfO07Pn7+RL5zI/J+9nPMACvH8qjZMFidmzZyrmCArZtrSQs4iyDn3uOCaWlVJhMLP7LXxgxdSrh4eEtJsXw9ha1nhv9hL8I/Bjw3KEPAyqllA7343OAJydiHHAWwP18lXv9VuW5KouMjOTb3/42M2fOpAbYsGsX+/bubV5HUZT2pdpo5MX/+i/04GDiVq3knTNnONrYyA9+9CNEfT1B907lYd7jV9OWcT4kxNvFvWWCgoMZPWUKj//61/zr+9/nvNFIYm4u4ZMns+Wpp7jtySf4Tl4ej9TUkFjXgG1IJoZt2zB0kJry1Qgh8Pf3J+5nP2Pv976HEXin6CK+r/yNgH79CPvtb+mxbBm3lZRQqmnM/8lPGD9rFjExMV3mHH7dwCyEmAmUSCmzb+UbCyGeEkLsEULsuXjx4q380wQHBzPrjjtITU3l4MGD7N+//5b+fUVRbq0Gq5Wza9ci/fxIKykhUcrPT05S8t2nG/hwfyYOR9vkvW5tzU27AQHExMfTZ+hQfnfffRzx9yelpoZH1q8nxmajLznsYxBSCOTTT3+eZrMDxydPJcrHxwfHAw/w/IABGIFvlpURUltLX3I4wCB04F+RkQTFxODn69ul+svcSI15JDBLCJGPq7PXBOAlIFgI4bl5Gw+cd/9+HkgAcD8fhKsT2CWklK9JKTOllJlXSlp/NZ7OPVfjmQxj6NChzJ49m8TERGxfmPVDUZT2Rw8ORgQFXRZzBBCxfB633VbBiRNjvFG0VuPj60tEVBTDRozg9nvv48VHHqWez0/ME1hHEz68Lr+Oz29+i0Bw8CBsvu6Mvu2fwWAgMjKS7J49qTGbXcf9hzDhIdc2v8E3+Hp5OaGBgeQe92HLls4fkD2uG5illD+VUsZLKZOB+4F1UsqHgPXA3e7VHgUWuX9f7H6M+/l18hZ2g/XRNHxNJnzMZkxXSdIuhCAwMJApU6YwevRogoKCsFgsbTK3sqIoN+eS00Nh4ZVXOnuWRx4ppbGxc2X08wiLiWHk+HE8/Y2v03L6Ds+8vmuYROqZtfTpAz/9KURHe6mgt5jdbsdmszXPY4wfiN6fb/MI2xGe+vEMfvErS6fZ5hvxVborPwPMF0L8FtgHzHUvnwu8J4Q4AZTjCua3TI+kJCaPGYNusRAfF3fVGYCMRiMpKSnMnj0bq9VKRUUF/fr16zI9tBWlo7jkYjkhAQo+nyWvFlcCCT0ujogIna997Wnmz58PLG/jUrYug9GIr58fCfHxNEZGYi0paX6ueV7fpCTIyfdeIVuBpwW0MjCQkOpqiAKMn29zsdnMrt+9xvg77sDfPQd9V3BTUUpKuQHY4P79FDD0Cus0Aq0ywFAIQf+hQ0lIS0MaDISFhV11Emtw5aIdO3YsGRkZNDY2Ehoa2pxoQ1GU9kf87nfIp55C1Nc3L2syGin57nc7/TScBk0jMjwc/vpXeOopaLEP8PWF557zXuFa2apx47hn9Wo0U0PzMrvJxFtxcfTxYrm8pUNVH4UQBIaFERh2aSdviXRljvgCg8FASEgIIZ2oJ6eidDaXNGW7ZuZBPvwwSEmh0cjfY2NJ9vOjX3l5pw/OQPM+4NlnXa0HiYmuoOxZ3gkd6NOHKVOmEKx9B3SJTExk1ahRrM/N7ZKBueMOCGxJuv5psjdSXlGuOnspSgdyWb+PBx+EwECcfn48MWYM86XkUHY25/Lzu04fkYcegvx8Vz7w/PxOHZQ9GufMgcBACA7CefIkxwcP9naRvKZzBGbApts4cDqbl155kePHj181IbyiKO2bcOc/1gwGUnv2pLq6mq0bNpCzfz+dOquEorh1msDs1B0Ulp1l246tlF4sU/mQFaWDE8DIUaOIiYujqqmJ8xcvUllZ6e1iKUqr6zSBGVwt2k7d6ZpCTMVlRenYhGDKbbeR2qMHVU1N5J05Q3FRUddpzla6rA7V+etGCDp2VhxF6Wqu1bpltVoZMGAA2dnZZO/di9aB82Qryo1Sn3JFUbzqajVgAZhMJrp3705ERAR1dXXU1dWpGrPS6anArCiK11wvxa7RaCQ9PZ3k5GSMRiMOh2veHBWclc5MBWZFUbxCSol+A500U1JSuOuuuxjcYvjM9QK6onRkKjAriuIVmhBYDQbMJhNGo/GK94+FEFitVhITE0lMTMTPzw+z2YymaarWrHRaKjAriuIVJk0jzGIhyM8PP6sV41U6dlksFrp160b//v2Jjo4mKCjomql4FaWj63S9shVF6RhCgoOZNGkS3QcMoE9GBv4BAVdczzNb3NixY/Hx8aGmpoaYmBjVQ1vptDpVYJZSd2X8ku782UqXIqVE112fgSvdf9SlxOl0IlVWuHbB4utLaq9ehNTVER0bi4+Pz1XXNZvNJCQk4HQ6aWhoIDAwUDVlK51WpwnMmtAIsAaSmJCIr5+v+tJ2QUajkejoaHx8fLBYLJc8ZzAYSEpKwlJXh9XPT30+vEwIgW9AAL0zM29ofU9gTkhIaOWSKYr3dZrAbNLM9EsZzDefCiMtNU01c3VBgYGBzJ49G5vNRmxs7CXP+fv7841vfAODrpOcmqo+H4qitFsdJjBfbWpHD00YiAqOIWpYzOevkVLVjLoIIQQWi4VevXo1L2vZnG02m8kcMqR5XaV9uux77j6G6pgpXUmHCcwAOk6kBCGv/yVVNaKuSUpJU2Mj9bW12BwOEAKTwYDBx4cAf38M6nPRYXiO5RdvSyhKZ9dhArPUdS7WFlNeWoFsFK7gfIX4LKVEaBphoaH4+vqiCYHJZMJkNgPqyrsz8iSbsNvtlBQXc+zAAbJ37ORCZQVCCML8/Ajt3p1J48eTFB2NyWzGYDSqz0J7JF0X4ELqOJ06JSUlxMfFYzAavF0yRWkzHSYw2+12dmzdyZLFS6gqrUHqrmbqK/W+FUIQFByMr8VCdGAgWcOHM3b6dDSD+nJ3Np6e2MWFhaxbv57169dz4uhRTp84QUV9PUII/M1mrNHRbF63joz4eKbMnEn/4cMxmkwqOLdDDt2BQerYbDZyj+cSHR2tArPSpXSYwOx06pzJO8u6FRsoLi7Gz9eX4JCQy0+sug41NVTbbDRKiZ/JxKZt26h2OpkxY4Y6GXciUkp0p5Mzubl8/O9/89HSpZw7fZrExCjueGQ2CbHdMWkmGhsb2bNnD1s2bWJjQwPZOTl86/vfZ+zYsZjU56Edcg12lFLS2NSkUm8qXU6HCcwAUnc1V4qmJrJGjGDqnXdisVpdY5c9dB1qaymtqSEnL4+tW7eybfduYubPp19aGim9eyNUzbnTqKurY/369bz97rvYDAbmzL6TseNHMHTcaMJDY9CEAYfDwdGjR9mwYQPvvfce67dtownXEKpx48YB6hZH+yMBqcacK11ShwrM4B7/qGn0TUlhyuTJhISHX7qClKDr2BwOTp44gb22lkVLl7Jz5062bN5MbGoqVl9f7xReuaXq6+vZt38/C5YsocZu5/Zp03jy8cdJ7Z5KQGAgBqOpuYfvgAEDiIuLw2q18tZbb7Fnzx4WL15Mjx49modWqeDcnriOhTokSlfU4QIzuL6yFrOZoIAAQkNDr7iOlBKz0UhWZiYHs7M5eeECh06eZI572jilY5NSUlFezubNm9mxcyfDhw/naw8/TN/+/fHx8fk8yLr/s1qtxMfH87WvfY2Kigr+8Y9/sH79enr06METTzxxzaxTiqIobanDjh253l0nIQRGHx8CY2Pxj47GqevYnU6VqLOTaGps5Mj+/WRv2wZAv4wM+vXrd2lQ/gJN0wgJCWHKlCkMGzaM8+fPs3PnTux2e1sWXVEU5Zo6bGD28AyVudJPU1MTBWfPcqagAIOPD2Zflaqzs6ivrWXXpk3s2LCBkNBQUpKTMZvN1z2+mqbRp08fevfu7epc1NiIw92KojoZKYrSHnTIpmwAXXcNp2hobGzODuQhpaSyspIzZ85w7NgxyisqSO3Xj96ZmRjd45mVjs2u6xTV1VHpdJIWG0tqaiqGG+jUJ4RA0zSMRiNOp5OKigoKCgoIDAxUSWkURWkXOmRg1oH8c+dYt2kTPi16ZQtcJ16n0+m697hjB+fPniU2MpJ7br+d28ePx6ICc6cgAYeUCIOB6OhoEhMTbygwN7/ePdNUaWkpp06dolevXiowK4rSLtxQYBZCBANvAH1xnROfAI4DHwLJQD5wr5SyQrjaEl8CpgP1wGNSyr23stA1us7KLVvYffQomqa5B1aAp1uXpmnouo7Fx4dBvXoxbswYHnjySUKDg1VTdmfRopXEoGkY1RA4RVE6iRutMb8ErJBS3i2EMAO+wM+AtVLKPwghfgL8BHgGmAZ0d/8MA151/3/LSMDfYiEpJMR1QpYSO1Dc0MDJ/HzsdjsTxo3jwQcfpE96OlFRUUTHx9/KIiheJoTAgOuWRmVFBaUXL5KSkgI3WOsVQmAwGAgLCyMlJeWmatuKoiit6bqBWQgRBIwBHgOQUtoAmxDiDmCce7V3gA24AvMdwLvS1ZNmhxAiWAgRI6UsvFWFtgjBqMxM7r7/fvwDAlzNklJSUFrKB/Pns3fPHi5evIjD6SSxe3dCgoM923KriqB4mclgIDIoiECrlXPnz5Obl8fAwYMxGK/9kfbk1Lbb7WiaRnBwMPH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      "text/plain": [
       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "4\n",
      "real/acs/5.png\n",
      "gold: [2*]c1cc(C)[p-]c1[2*]\n",
      "post: [2*]c1cc(C)[pH]c1[2*]\n",
      "grph: [2*]c1cc(C)[pH]c1[2*]\n",
      "pred: CC1PC([R2])=C([R2])C=1\n",
      "(785, 977, 3)\n"
     ]
    },
    {
     "data": {
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HJ2RNFnO3NebQ5CZqpVRp7L7wAIKB8inilXOE3YSIzAPmwc2LWMyfP5/ffvstw8YcP36c7du3pwqzWCz079+fBg0apBpdrZTitdde44knnshw/inRtWWNJnvo3LkzzZs3Z/Pmzc7lHs+ePcuJEycoN3cudOsGXbvaFeH77+1DsgMC3Gt0PmHzZvDwsH8fJVOnjvvsyevcrTCvAwYAkxx/16YIH6qUWg40BiLupn95y5YtbNmy5S5Ns4vyI488wvTp0ylUqFCqcyaTic6dO9913hqNJutRSlGgQAFeeeUVfv/9d2Jj7cPBrFYrX3/9Na1bt4Z27eztqa1b26tspUvDypWQgXXVNZnj0CFo0MDdVuQfbtuUrZRaBvwOVFNKXVRKPY1dkNsppU4CbR3HAD8C/wCnsDvzeT5brL61nSilaNWqFVOnTmXBggUZ9s+r0Wjcj1KK0qVLU6NGjVTh586d4/Lly/Zpj/7+sGcPjB8PCQnwyCMwcCA4atiarCO5q9AwDC5evMi1a1fdbVK+IUeux1ysWDEKFPivLyD73OPLly/fFN66dWteeOEFwL4Axn333ZdNlmo07iU/rMd84cIFGjduTEjIv41tNWrUYP369ZQvn6Kn7K+/oG1bCAuD8uVh+3aokJa7Ek1GSdYE6+LFhA0ZQrG4OL5WbXnTbwonwmtrx0sZI1MPKUd60pgwYQL79u27afv222956KGHePDBB/FOMW3C29ubkiVL0rlzZy3KGk0up3jx4hQpUiSVAJw6dYqIiIjUEevXtzdtt2sHFy5A5crwxReICNHR0URERBAVFeXsr9akj4gQFRXFtWvXmNawIdb/+z9KxMVh8oQ+spGAyHje7vLvUroHDsC2bW42Oq+S2VUwsmLjPyvNfPnll7dcrsMwDElKSpKYmBgZPXq0mEwm5ypSPj4+8vLLL0tsbGza631oNLkcctnqUoUKFZLQ0NA7ukebzSb79++XKlWqpHovvPTSSxIZGXnrRJ98ImI2i4DYOnWS3t27S61ataR9+/Zy4sSJO7p+fsMwDLl+/bpcvHhR2rZtK0FBQXLWPhBbpCIipRAxIcGUlofVCilfPkFq1hTp3FlEP9o0yVwZymwGWbFlVJhTcvXqVRkzZox4e3s70/n4+Mg777wjYWFhd/4YNZpcQH4QZhG7OL///vvy37xGjRqVdqJ//hEpW1YE5IbZLPeBeHh4yGOPPSY7d+6UhISEO7YjLxMXFye//fabfP/99/LAAw9ItWrVpKnJJL+CGMnCbEGkAyKD7cc2kFdffdXdpucGMlWGcmRTdkYoVqwYo0ePplq1as6wuLg43nnnHaZOnXrbpR41Gk3OxWQyUbt2bcqUKeMMi4iI4K+//iImJubWie65B86fx3jySQJsNvYDryUl8e2339K3b1/WrVtHRERE8sdDviL5hW+1Wtm6dSsbNmxgzZo1PPbYY/R5+GG6//47244fZ5th8BApHFNYgW5AC6AknMfukOnQoUP58jm6jMwqe1Zs3EWNWUTEarXKypUrpUiRIqnSly5dWoKDg8UwjDv4wNFocj7kkxqziL3W/O2330rhwoWd+VksFjl27Nhty/b5jz6SJEfT9u8g3o70gwYNkvPnz+erd4NhGBIXFycTJ06U4cOHS4ECBQSQR0D2OGrBApIEcqpcOfnyuecket48EV9fe625ISJLENtE5AnHc6xTp45cuXIlXz3HOyRzZSizGWTFdrfCLCKSmJgo77zzTqr0FotF+vTpI4mJiXfwHDWanE9+EmYRkfj4eAkMDHTmZzKZZOTIkWn3NafAuHJFrpUsKQISBdLCkUefPn3k7NmzeV5UDMOQmJgYGTdunDz//PPi4+MjpUAWgkQ6xNgAuQCysVUrmT1tmkRERPybwZIlIhUriiglMtVDjCXIkEdKOcf1jBs3TmJiYtx2fzmc/C3MIiJbtmyRoKCgVHncc8898vfff+f5wpcbMAxDrFarWK1W2bFjh3zzzTeSlJTkbrNyJflNmG02m7z55pupyravr6+88cYbGUofGxsrwU88IYZDhGY48qhbt64MGzbsru3K6URERMjDDz8sDzzwgFhMJnkG5FiKvuN4kJ88PWXjrFmybds2iY+Pv02GJ8RYqiRigcX5f7j//vvl+vXrrrmh3EemylCOnMf85Zdf0u8OFloVEfbv30+nTp1SzXOuVq0aS5YsoWHDHD3tM88iIsTExBAXF8eIESO4cOECx48fx8/Pj99//50A7U7xjskN85h/++03Z3m2WCw0atQolQ/sOyUkJITjx4+nCitZsuRNjkjSQkQ4/vnnBA4dimdCAseBZoCtcGEaNGjAqFGjaNOmzV3blxNISkri0KFDhISE8NFHHxEVFUXkH38wwTDoAHhhV9NzXl4sL1uW+6ZNo1iJEjRo0CDD/xtja09MF1excHcpBs24jJ+fH8899xzvvPPOTesRaDI3j9ntX9eSBTVmEZHo6Gjp1auXmM1mZz5KKZk2bVqGa2fR0dHy008/yffffy/bt28Xm812x3ZoRK5duyY//PCDrFixQho2bCiBgYHi4eHh/L94eHjICy+8IJcuXXK3qbkOckGNOfvuPnMkRUTIiRIlxHDUGLs5fo9NmzaVnTt3pm7GzSUEBwfLzp075ccff5SSJUuKn5eXjHM0TyfXjiNBvitWTKYMGyZhYWESFRV1d++2pDgxlnmJbalZ6t5XSQDx9vaWo0ePZv2N5X4yp4mZzSArtqwQZsMwJDQ0VFq3bp0qrxIlSshXX32VoSbtEydOiJ+fnyilpEKFCs4pFro5PH2SkpIkPj5ejhw5IlOmTJEhQ4aI2WwWR0uIU4w9PT1TzT0fOnSoxMXFudv8XIUW5rvHMAxJTEyU088/L1aHcC0HUSCenp4yZ84c2b9/v7vNzBAhISGybds2GTp0qHh7e8tDZrNscQzgSp7WtN/DQ16vUUNGjx4tkZGRkpSUlPl32eEPRJYiFxbf5+zzX7dunVit1qy5sbyDFuaUzJgxQ7y8vFLl17p16wwNUjh9+rSUKFHCma58+fLStWtXOXfunBaQ/xAVFSWhoaESGhoq48aNkwYNGkjlypVTPXellBQtWlTKlSsnq1evlt27d8uAAQOc4ly2bFk5c+aM/vC5A7QwZx7DMMQ4fFgifXxEQIJB7knR93zjxg13m5gmJ06ckE2bNslrr70mhZWSGSDXUgzkugIyz99fOjVvLgsWLLDfa1aXr9WlxViKNKlmf8/Wr19fDh8+nLXXyP1oYU5JVFSUTJgwIZU4e3l5yZo1a26bNikpSb766ivp0aOHM73ZbJaSJUvK8OHD5dq1a3dtV17hjz/+kEWLFkm/fv2kVKlSUqpUKfHx8Un1/ytZsqT069dPBg8eLEeOHJGQkBDnCPmQkBDp16+fmM1mMZlM0rhxY90UdgdoYc46jMRECX/oITEcNc3nHb/f+vXry9ixY3PUB+Ps2bOlTZs2UqFCBekG8heppzltAnmnc2fZsGGDnD59OnsEOZnLm8VYikR8WUwKFiwogAQGBso///yTPdfLnWSvMGNfX3kzcAQ4DLzsCC8CbABOOv4WdoQr4CPsK0wdAOpn4BpZJswiIpcuXZJy5cqlyrNbt27OH2x6GIYhkZGRMmLECPH393f2WZvNZhk4cKBs3rxZoqOjc1ShzS4Mw5Do6Gi5ceOGTJ06VUaNGiX169eX//6/vLy8pGDBglK/fn2ZMGGCLFmyRGw22y1fDoZhyJUrV6R9+/bO9J07d9aFOoNoYc5arFarnHjrLUlSSgyQX0EsIMWKFZOnn35aLl686JYZBLGxsRIeHi5TpkyRdu3aSaPSpeVLkOgUteNzSsmbXl7ywnPPyfr16+X06dOuM/DnB0SWIsO6+jubtHfv3p0v3osZJNuFuXSyuAJ+wAmgJvABMMoRPgqY7NjvDPzkEOgmwK7bXcPT01MqVark3DJSu02PpKQk+eGHH6RKlSqp8n3uuecyXMji4uLk2LFj0q1bt1TN28WLF5e2bdvK0aNHJTo6OlN25lTi4uLkzJkzcuDAAWnatKlUqVJFPD09bxLk4sWLS6VKlWTatGly/PjxO3LqMm3aNPH19XV+9KxevVoPtssAWpizHsMw5MzOnRJRrJgISBhIQ+wufuvWrSu//PKLy9x5RkVFydWrV+Wtt96SBvXqyesFCsjJFAO54kDWeXhIk6JF5b333pPdu3dn+SDKZN/ZUVFRaUeKCRZjqUkSvvQQDw/7WJKnn35au0P+l+wV5psSwFqgHXAcKC3/ivdxx/6nwBMp4jvjpbXVrVtXwsLCnFtWFIKkpKRUeYaFhUlkZOQdfdEZhiFRUVHy559/SrVq1ZyCZDKZpFChQvLcc89JaGhorv9KTK7Znjx5UqZPny4vv/yyFC5cWPz9/VMN4EquHT/99NPyyiuvyI4dOyQsLOz2cyBvQWxsrEyZMsXZZRAYGCg7duzIhrvLW2hhzj6ioqLkVJs2Yjiaid/lX58In3/+ebZ5DEsuezt37pQXXnhBut17r/xisUhCitrxEZA3KlSQKR98IPv27ZP9+/dnyxxiwzDkn3/+kYceeui2gzONPwaLLEW+f6O0mEwm8ff3l4EDB+bZCssd4jphBiphd5fqD4SnCFfJx8D3QLMU5zYBDW+R12BgD7CnQoUK2fyMModhGLJx40YZO3aslChRwjn1x2QySbdu3eTTTz+Va9eu5aqRiYZhSEREhFy9elU+++wzGTx4sLRr1+6mWrGnp6cULVpUAgMDZfr06bJw4cL0v6TvgMjISBkzZoxzMNiYMWPuSuTzE1qYsxfDMCT0yy8l3mQSAfkbxN9RFp555hk5dOhQlorzqVOnZOfOnfJo+/YyHuRSitpxOMhnSkmLGjWkY8eOLqkE2Gw2ee+995wzKdasWZO2ONuSxPi6gBhLldxTytv5Tty3b1+22phLcI0wAwWBvUA3x3H4f86HyR0Ic8qtQYMG2fd4spCEhAS5cOGC9OvXTypWrOgUL19fX6lSpYrs2rUrx9eek5KS5OjRo7J//37p1auXlClTxtmknHIrUqSIBAUFyZAhQ+TixYsSEhKSLU3Ne/bskeLFiwsg/v7+smDBglz1geNqtDBnP4ZhiBEeLtcqVBABiQHp4CgX5cqVk/Hjx99yKcmQEJHevUUCA0Xq1xfp1Enk+PGb8//mm29k/PjxMn78eBlcubJsB7E6xNgK8gfIGw0byptvvimTJk2Sy5cvZ+9grhSkFObkFrJTp06lneDkfJGlyJHp/s6ZGC+99JJcuXIl223N4WS/MAMewHpgWIqwLGvKzi3CLGIvtPHx8XLw4MGb3IDed9998tZbb8mlS5dyjEAnF+iwsDCZMGGCvPbaa1K8eHHx9vZ21lSTN6WUPPHEE/Luu+/K2rVrJTY2NtvncVutVlmzZo1UqmR3WFCsWDGZM2eO7m9OAy3MrsMwDAl/+WWxOWqxn6UoK6+++mqqcmEYIk2aiMyd+2/6fftEtm7993jp0qUyYsQIaVK1qnwMciNFU3UIyPsgfR59VCZPnix//fWXs+y68l1iGIZs2bLFuXCI2WyWefPmpZ9mbWUxliLdmwUI2NcqmDZtmosszrFkrzA7mqkXAzP+E/4hqQd/feDY70LqwV9/3u4auUmYkzEMQ37//Xf54IMPpGLFis6+UpPJJG3btpUpU6bIhQsX3OYTOioqSs6dOyffffed9OjRQzp37pzK+1ZyoStbtqxUrFhRJk6cKCtXrpSLFy+63NakpCTp2rWr064mTZpIeHi4y+3IDWhhdi2GYcj+BQskzjHn+axSUgYkICBAunTpIqtWrZIzZ87Ixo2GNG+eOm1cXJwcOXJExo4dK126dJHnAwLkQIqm6kSQzZ6e0r1SJXnrrbdk3bp1zsU13PVhn+zX/t1333W+LypVqiRbU35h/DfN9b/EWIpELfJzfuy3bds2V3pSy0KyXZibOV6YB4B9jq0zUNTRTH0S2AgUkX+FfA5wGjh4u2ZsyaXCnExSUpJcvXpVnnnmmVRzpz09PaVcuXLy008/ubRp9vz587Jt2zYZN26cFC5c2LnEW/JmsVikYcOG0rRpU3nsscfk/PnzcuPGjWxZiSspKUn+/PNPOXv2bLrxDMOQAwcOOFcRslgs8uabb+b3gn1LtDC7h6SYGElo3lwMkASQpxzlqUCBAtKyZUuZNClWXnnFHvfs2bNy4MAB+fbbb6WOv7+sUEpiUtSO/wF5v3hxqV2zpixZskRu3LiR48ZWbNiwwdmKZTKZZNq0aekPyt3UQWQpMql/gPP998knn+S4+3Ih2SvMrthyszCL/Lu82uLFi6Vr166pmoiLFi0qzz77rOzatUusVmuWfgknf90mJCTI1KlTZciQIdKkSROxWCypbFBKidlsllatWsnnn38uYWFhkpiYmDUu+tLAarXKhAkTJCAgQJo2bSoXLlxI91qGYciOHTukTp06TnF+8cUX9dKd/0ELs/swDEPOjhwpSQ6BXQfSF+QsyAxelBd9P5XT774rndu3l9c9PORMitpxDMgKpaRNYKC0b99eTp06JdHR0Tl2lbWkpCRZunSpc5pksWLF5Pfff087QUKEGF9ZxLrUIoX97RWU0qVLy5YtW1xndM5CC3NO4syZM7J69Wq5//77pUiRIqn6n59++mk5cOBApqYTGIYhZ86ckcOHD8u+ffvkqaeekoceekgCAgJS1YwDAgKkRo0aUqNGDRk2bJhs3LjRpR62EhMTpVmzZs4v7ocffvi2NWDDMOS5555zflRUq1ZNTp06lWP663MCWpjdi2EYYjt1SsJ8fUX41/vWRlpLc7ZIEv/6qzZADoA8ZbFIvXr1pHXr1nLs2DGn852cTnx8vHTp0sUpzl27dk2/DP89WmQp8ssb/s73UNeuXV1mbw5DC3NOw2azSUxMjEyfPj2Vu0qTySQFChSQWbNmZbi2mtzfFBsbKz///LOsXbtW7r//fvH19RUfH5+b5hlXqVJFunTpItOnT5fo6GiJjo52mXOElFitVnnllVec91+0aFHZsWPHbWvN58+fl9atW4tSSpRS8tBDD0lISIgLLc/ZaGHOGdiSksTm6HcWEMMDacQf8inPihXkY5AqJdrKO+9slg0bNojVas01gpxM8nsnZRfTxo0b005gs4mxsogYS5HmdUo6a8351I+2FuacSnx8vKxbt06efvppKVWqlLMmWKhQIenevbt8//336c5NDAsLk71790q/fv3kkUceES8vr5uEuESJElK+fHmZMGGCLF++XPbt25cjXgDJo9fHjBmT6qNh/fr1t013+fJl5yphSin54osvdJO2Ay3MOQilnMIsIMGUlp58LYGcknLlIqRRo6ty/HjuEeJbYbVaZfTo0c7V4lq2bClnzpxJO8H5b8VYilz5ooTTnXGLFi3kyJEjLrM5h6CFOacTExMjx48flwceeCCVsBYsWFCaNWsmx48fdwrphQsXZOfOnbJz504ZMGCAFCpU6KY5xj4+PtKoUSNp0aKF/Pnnn3Lt2rUcK1ybNm2S8uXLO20fOXLkbQeEGIYhEyZMED8/P2dt+8cff3T7x0ZOQAtzDqJiRfsr9FVERv8r0PGlSuWp3+qFCxekY8eOzlaskSNHpr9a3w+1xViKjO1f3VnuX3zxRdcZnDPQwpwbMAxDLly4IB999JFUr17dKdBKKalVq5YMHTpUhg4dKk2bNhUPDw/x8PBItXaxyWRyet/68ssvJSYmJlesFW2z2WTLli1SpkwZAbszltGjR6crzsnr5k6fPl0sFosAUrFiRVm+fLkLLc+ZaGHOQSxZIuLrKzIP+waS5OVlD89DGIYhf/75p5QqVUrA7iP/7bffTjtB1D9iLFUSs8hTPD3t5bdly5Zy7tw51xntfrQw5yYMw5Ddu3fL1KlTpVq1ajetHf3fZupq1arJyy+/LOvXr5ddu3blSscbVqtVvv32W2cNuECBArJw4cLbpjty5IhzygYgTz75pMTGxrrA4pyLFuYcxpIlIvOVGPOQMyB/Js+ZymOEh4fL/fff7yyLjzzySPof19t6iyxFlg4v40zTqVOn9GvaeQstzLmR5EU2evfunap522QySdOmTaVjx46yYsUKiYqKcsvgrazm6tWr0rhxY+d99uvXT0JCQtKt8VutVtmwYYNzdS8vLy+ZM2eOxMbG5viWguxCC3MOZEUhifrCXjP85ptv3G1NtmAYhmzfvt25nK7ZbJaZM2em7Uc7KU6MZd5iLDVLjcBSzpa/P//807WGu49MlSETGrdgsVgoVKgQM2fO5JlnngGgQYMGLFmyhHXr1vH999/To0cPChYsiKenp5utzTxFixZl0aJFdOjQAYClS5cyZMgQIiMj7V+It8BsNtO6dWu+/PJLypYtS0JCAq+99hqzZs1ypekaTb5HKUWjRo2oWrUqADabjaVLlxIbG3vrBBZvVJ0JKGwsHhwH2CuB69evJyEhwVVm51q0MLsRpRQlS5akUaNGAJQvX57evXtTpEgRzGYzSik3W5h1KKWoVq0aH3zwASVKlEBE+PHHHxk2bBg2my3NdCaTiRYtWlC7dm0A4uPj2bhxI5cuXXKV6RqNBvuH8siRIylVqhQAp06d4uuvv047QY1hiE9ZGpSP4NGmpQGYMWMGixcvdoW5uRotzBqXEhgYSIcOHbBYLCQmJrJjxw5OnjyZZq0ZwMvLi0mTJtG4cWMANm7cyOuvv05ERISrzNZosoSEhAT++ecfLly4kO5vPidiMplo27YtQ4YMoXjx4ty4cYOZM2eyf/9+DMO4ZRr14FcALHzWXmu+fv06U6ZM4dSpUy6zOzeihVnjUgoUKMCcOXPo06cPAMePH6dPnz4cOnQozTRKKWrVqsVXX31F9erVERG+/fZbzpw5k+tebpr8zcmTJ2ncuDHdunXDarW625w7xmQyMXLkSN555x3AXn7HjRtHXFzcrROUbIEq1ozCHuEMf8wPgBMnTnD8+HFddtNBC7PGpSil8PPzo23btpQoUQKAQ4cOsXPnznSbtJVSVKhQgYceeggvLy/i4uJ4+umn2bdvn4ss12gyh9Vq5eLFi4SFhREZGeluc+4aLy8vSpUq5Rz7cv36dW7cuJF2gharEUxM7JmIj48HAJMmTeLs2bMusDZ3clthVkp5K6X+VErtV0odVkq97Qi/Rym1Syl1Sin1tVLK0xHu5Tg+5ThfKZvvIV9htVpJTEzEarXm6i/O/v37M3r0aHx9fTEMg3fffZfff/89zSYxsPdxTZ48ma5duyIi/PXXXwwbNizd2rZGk1MICwtjzJgx6X6A5hY6d+5MixYtMJvN7Nq1i/fff5/4+PhbR/YuAVUGYyGBtWNKYTKZ2LFjBwMGDCAmJsa1hucSMlJjTgBai0gdoC7QUSnVBJgMTBeRKkAY8LQj/tNAmCN8uiOeJouYOXMmLVu2ZMyYMblamE0mE0OGDGHGjBmYzWaCg4Pp27cv27ZtSzNNcm27d+/e+Pj4APDbb7+xcePGdAVdo8kJiEieEGUADw8PvvzySwYNGoRhGHz++efp9hurhnMQix9tAy9yb1lfRITt27dz8OBBF1qde7itMDvmZEU7Dj0cmwCtgVWO8EXAY479ro5jHOfbqLw0vNiNXLt2jY0bN/LHH39w7Ngxd5uTaby8vGjWrBmBgYEAnD9/nl9//ZXExMR003Xs2JFp06bh52fvs5oyZQqrVq3KMy89jSankzyjpHHjxiilsFqtrFy5Mu2+ZpMJ1XA2CuHHMd6YTCZEhIkTJ3L8+HHXGp8LyFAfs1LKrJTaB1wBNgCngXARSR69cBEo69gvC1wAcJyPAIreIs/BSqk9Sqk9V69ezdRN5BdOnjzJpk2b3G1GllK9enU+/PBD7r33XgA++ugjFixYkG4N2NfXl//7v/9j5MiReHt7ExwczEsvvcSWLVtcZbZGowHatm1LlSpVEBE++eQTJk+enPYHcuBTSMEq3ON/jWcfrgjAd999x8yZM11oce4gQ8IsIjYRqQuUAxoB1TN7YRGZJyINRaRh8eLFM5tdviE3N1/fCqUUjz76KLNnz8bT05Pw8HAWLVpEVFRUuuk8PT0ZOHCgcwBZaGgo69evT/uLXaPRZClKKSpWrMiYMWMoWbIkV65c4ZNPPkl3UJdqvhqA6b2uYbFYEBEOHDhw2/Ke37ijUdkiEg5sBh4AApRSFsepckCwYz8YKA/gOF8IuJ4VxmryJkop7rvvPurVqwfAnj17GD58OCEhIemmK1WqFB9//DE1a9YEYPbs2bz77rvas5BG4yKUUvTv358hQ4YAcPXqVU6ePJl2d1Th2qgynfFRUUx4wheAnTt38vnnn9+2Cys/kZFR2cWVUgGOfR+gHXAUu0D3cEQbAKx17K9zHOM4/6vktWqeJsspW7Ys06dPp3HjxthsNj7//HMmTZpEUlJSmmnMZjNdunRh0qRJ+Pv7Exsby5IlS7h69Wqea1nQaHIqJpOJhg0bUrhwYQzDoGfPnixbtiztBA8uQ5QHwzvGUbFscUSEt956i4ULF7rM5pxORmrMpYHNSqkDwG5gg4h8D4wEhimlTmHvQ/7cEf9zoKgjfBgwKuvN1uRFmjRpwty5cylYsCAiwi+//JIhD0nNmjWjevXqKKW4dOkSgwcPTn9epUajyVI6dOhAz549UUoRHR3N9OnT0x6l7emPum8UJpJY9Jx9mFJkZCQrV650ocU5m4yMyj4gIvVEpLaIBInIO47wf0SkkYhUEZGeIpLgCI93HFdxnP8nu29CkzdQSlGpUiX69OmDUooTJ07Qs2dPTpw4ka44BwQE8PXXX9OiRQtsNhvr16/n+eef5/z58y60XqPJnyilMJvNdOzYEZPJLikHDx5MfypU0HjEsygtKofxUEO7H+2jR4/q6VMOtOcvTY6icOHCvPvuu7Rq1QrDMPjrr78YOnQowcHBaaZJFvSOHTvi7e2NYRisXr2aAwcO6CZtjcYFKKWoU6cO//vf/7BYLBiGwbJlyzh9+vStE5hMqCYLAVgx1IqPjw/BwcGMGTOGkydPus7wHIoWZk2Oo0SJErRu3ZqCBQsCsG3btgyJ7NChQxk4cCBmsxmbzcYbb7zB0aNHtThrNC4gMDCQDz/8kGbNmmGxWFi1ahXDhw9PezBmuYdRhetR1OMqk4bYB35+//33ecJHQ2bRwqzJkbz++uu8/fbbeHh4kJCQwAsvvMDKlSvTFdkCBQowYcIEXnrpJZRS7N+/n7ffflsLs0bjIry9vVm1apVzbvP333/Pnj170k7QfA2geLbOHjw8zAB8+OGHXL58OV+XWy3MmhyHUgpPT0+eeeYZRo4cCcDZs2cZNWpUul/TSimKFClC7969CQgIQETYunUra9eu1VOoNBoXoJTC19fXuWaz1Wrl9OnTafsXKFgRKj6JjyWRr4bb+5p37NhBv379iI2NdZXZOQ4tzJoci7+/P+3atXM6EUl22RkfH5/u13SDBg0YOXIkAQEBXL58mUGDBvHjjz/m6y9wjcZVeHt7M3v2bIKCggB47rnn0vXupRovQEzedA8K4cH6VTEMg82bN6df087jaGHW5GiaNWvGokWLKF++PDabjeHDhzN79ux001gsFl577TUmTZqExWIhIiKC2bNnpzsnWqPRZA1KKWrWrMnIkSMpWrQo8fHxbNu2LW0HIhZPVL0PUNj4pP8NTCYThmGwcuVKwsPD8+UHtRZmTY7GZDLRrl073nrrLUwmE/Hx8fzyyy+39QpmsVho1aoVVapUAezexN544w2io6PTTafRaDKPUipVa9fevXuZNm1a2n60q72I+JYnqMQ1+naoDMDixYuZM2dOvlw5TguzJsdjNptp3LgxDRo0AGDTpk2MHDmSiIiIdL+m7733Xj777DPKlClDZGQk06dP5+OPP87XfVcajasICAhg8ODBeHh4EBoaysyZM/n777/TFFrVdBkKmPOk3YNzVFQUc+bM4cCBAy60OmeghVmTK7jvvvtYtmwZ1atXd86RfOaZZ9JdaN1kMvHggw/ywAMPoJQiKSmJyZMn5/sRnxqNK/Dy8uL555+nYcOGAFy+fJnXXnst7QUrijeF4i3wM91g+KP29dZDQkI4d+5cviuvWpg1uYLklWxatmyJl5cXNpuNrVu33nYtV6UUkydPpmPHjgBEREQwZsyYdFfA0Wg0WYOHhwePPPIIRYvaV/69cOECe/fuTTtB89WIMjOpj0G5MvZm8FGjRrF27dp8Jc5amDW5BrPZzIcffkjXrl0xmUxcuXKFESNGcPDgwTQLrVKKwMBApk6dSsuWLbHZbKxYsYLXXnuNsLAwF9+BRpP/GD58ON27dwfs0x4nTJhAaGjorSN7F0Pd+zxmSWDd2JJ4e3tz4sQJRo4cycWLF11otXvJsDArpcxKqb+VUt87ju9RSu1SSp1SSn2tlPJ0hHs5jk85zlfKJts1+QylFH5+fsyePZunnnoKgM2bNzNw4ECuX097ZVGlFDVq1KBfv36YzWZEhJ9//plTp07ly4ElGo2rUEphsVh49dVX6dWrFyLC5s2bmTdvXtoDwerPAIs/dQMOcW9Zb0SEEydOcPz4cS5fhj59oHJlaNAAOneGEydceksZRilKKcVypTitFHuV4kelqJqRtHdSY34Z+3KPyUwGpotIFSAMeNoR/jQQ5gif7oin0WQZxYsXp23btvj5+QFw8uRJNm3alHZBd9CjRw+ef/55fHx8iI+PZ8CAAezatcsVJms0+RalFNWqVaNPnz7OD+P169cTHh5+6wQmE9z/MSCsG27Gw8MDgIULF9GhQywtWwqnT8PevTBxIqRV+XYnSqGANcBvIlQWoQEwGiiZkfQZEmalVDmgCzDfcayA1sAqR5RFwGOO/a6OYxzn2zjiazRZRs+ePXn99dcpWLAgUVFRvPzyy2zbti3dNIUKFWLSpEnOL/ejR4+yZMkSrFari6zWaPIvnTp1okmTJiil+PPPP3n66afT9i1wT1+UXzUq+l9nRL9aACxdGsy5c6f43//+jVanDjRv7gLj75yHgCQRPkkOEGG/COm/pBxYMniRGcDrgJ/juCgQLiLJb7SLQFnHflnggt0QsSqlIhzxr2XwWhrNbfH09OT1118nLi6O999/n9DQUH7++WcaN26Mj4/PLdMkuwt8/PHH+eabb4iKimL58uXUrVuXzp07u/gONJr8g1IKLy8vJk+ezBNPPMGFCxf48ccf+fvvv2nUqNGtEzVfjfoxiLfan2bqV14kJATh6XmQqKhK+Pn5kcPre0FAOqPc0ue2wqyUehi4IiJ7lVKt7vZCt8h3MDAYoEKFClmVrSYf4enpSZs2bfjmm284duwYs2bNwmKx8MYbb+Dl5ZVmuo4dO/Lxxx8zdOhQbty4wauvvsro0aNdaLlGk/9QStG4cWNefPFFXn/9dZKSkjh79iy1atW69cd0wH1Q9lE8g9cR+rBi4Wo4cPUKGwYNIuj9911/AxnA77vvKD59OjPoMeMY1aNQz+9G5Ks7zScjNeamwKNKqc6AN+APzAQClFIWR625HJC8YG4wUB64qJSyAIWAm0bmiMg8YB5Aw4YN8884eE2W8tBDD/HBBx/wv//9j0uXLrF69WpGjBiRrjB7eXnRqVMnSpQoQUREBDExMenOh9ZoNFmD2WymefPmlClThkuXLvHss8/yxhtvMHz48FsnuPgoYltHoUeFoLWHWW3tQcdvhvHyTz+xytPTtcbfhh6JiXwUF4cHEMQhtZru/sBnKMWdivNthVlERmPvtMZRYx4uIn2VUiuBHsByYACw1pFkneP4d8f5XyU/TUDTuBSlFJ07d2bZsmUMHTqUcePGOQeFpUVkZCRTpkzhzJkzAJQsWZJChQq5wtxMcyKnDkHVaDJAcq15wIABTJs2jcjISDZu3Ji2MI99F1Uf6AGt+//KmC/eZynPMi/uM6bExXGIWkRSiAfZ7tL7uBX+/DtoqzW/Mob3mcezvoP57H3gK6WoDRTKSD9zRvuYb8VIYLlS6j3gb+BzR/jnwJdKqVPADaBPJq6h0dwWs9lM06ZN2blzJ97e3phMaY9pjIuLY9SoUXz++edYrVYaNmzItGnTqFu3rusMzgT+/v7uNkGjyRRKKV5++WUsFgvTpk2jV69eaUc+fx7OAVVAlYE1PM4rzGAyI/EknvKcZSKvuMr0dFH/2U9ha8V/FIeBs5AxY+9ImEXkN+A3x/4/wE299iISD/S8k3w1msxiNpspWLBgunFEhODgYNasWUNiYiJFixZlypQpNM+hwzo1mrxKyZIlGTp0KPfffz/NmjVLO2KFCnDuHHwEJEAZQlhBb2KLF2fj/PkABDPV2Y/qTto+8wy+V686j5NtBc4hct+d5JWZGrNGk6sICQlh0KBBXLlyBZPJRL169ahfv767zdJo8iUlSpTgkUceST9Sly7Ixx+jEv4NEh8ffKZP59FHH81eA++U6dNh8GBIvUhOLDDmTrPSLjk1+YKTJ08yaNAgduzYgWEYdO7cmSVLlty2lq3RaNyEYWBbuhSwz781gNjixeGzz1B9+7rVtFvSty/MmwcVKwII9kb4Z+9mVLYWZk2exzAM9u7dy8aNGxERChYsSIcOHShZsmROnwup0eRbYseOxRwRwRqgAtClY0fO/vZbzhTlZPr2hbNnQcSESKW7EWXQwqzJ44gIu3fv5q233sIwDCwWC0OHDuXZZ591t2kajSYt4uPxnDKFJKA/ULp0aaZOnUr16tXdbZlL0H3MmjzN7t276dWrF+fPn8dsNvPmm2/y6quv4pnD5kBqNJp/MZ59FovVyhQg3mSiSY0a1KhRI9+0cOkasybPEhsby9atWzl//jxgX/yia9euFCxYMN8UcI0mt2ELCUEtXUoUMAKoV68eCxcudLNVrkULsyZPYrPZmDJlCm+88QYAVapUYcGCBQQFBbnZMo1Gkx7hnTqhRHgN8PHxYfz48ZQrVy5ffUxrYdbkOUSEK1eu8N133xEfH4+npyfvvfceHTt2TNf5iEajcTMHDlBk/34uAp8BnTt3pl27dvlKlEELsyaPISKEh4fz4osv8vfffwNQu3ZtGjdunO8Kt0aTmxARorp0AWAgULRoUdq2bZsvx4NoYdbkKa5fv87gwYNZs2YNNpuNBg0asGLFCira5xZqNJocysW5cyl48SL7gF+V4pVXXmHAgAH58oNaC7MmzyAiHD9+nJ9//hnDMPD29qZly5ZUqlQpXxZujSa3ICKUGGN3kNUbCAoK4plnnklzbfW8jhZmTZ5ARDh8+DAjR44kOjoapRQ9e/bknXfecbdpGo0mHUSEmPfewysigp+AU0pRqVIlSpYs6W7T3EaGhFkpdVYpdVAptU8ptccRVkQptUEpddLxt7AjXCmlPlJKnVJKHVBKaWfEmmzn3LlzPPHEE+zYsQOA559/nmnTpuHr66tryxpNDuZ6aCgeb7+NFegL9O3bl3nz5uXrcnsnNeaHRKSuiDR0HI8CNonIvcAmxzFAJ+BexzYYmJtVxmo0tyIpKYmdO3c61youVKgQHTt2pFixYvm6cGs0uYH4Z5/Fy2bjMyAcGDBgAKVKlXKzVe4lM56/ugKtHPuLsC8HOdIRvlhEBPhDKRWglCotIiGZMVST8xERNm3axNmzZzOVT+nSpenUqVOGpjYZhsHGjRt58cUXSUxMxN/fn+HDh9OxY8dM2aDRaLIf2/XrlPnhB2KAoUDbtm0pX768u81yOxkVZgF+UUoJ8KmIzANKphDby0Byh0BZ7IuBJHPREaaFOY8jIsyZM4dvv/32rvNQStGxY0c6dOiQIWFes2YNL774Ijdu3ABg3LhxvPrqq5jN5ru2QaPRZD+GYXC1fXtKifAmUKZcOT788EOqVavmbtPcTkaFuZmIBCulSgAblFLHUp4UEXGIdoZRSg3G3tRNhQoV7iSpJoeilOL+++/HMIy7zqNIkSK88cYbGRLW2NhYNm3aREiI/ZuvRo0aNG/eHItFu4DXaHI6e5cto+FffxEKzPf3Z82iRdStW9fdZuUIMvQGE5Fgx98rSqk1QCMgNLmJWilVGrjiiB4MpGyLKOcI+2+e84B5AA0bNrwjUdfkXEaPHo29F+PuUUrdtm84KSmJjz/+2OlDt3jx4kyfPp3GjRtn6toajSb7SUpKosrYsQA8A/j5+eX7fuWU3FaYlVIFAJOIRDn22wPvAOuAAcAkx9+1jiTrgKFKqeVAYyBC9y9nDR4eHvj6+hIZGeluU25Jspi6YsBVWFgYn376KXFxcSilqFGjBi1bttSDvTSauyAmJoaIiIi7Tq+UolixYnh4eNw2roiwb+pUGp47x1HgF09Ppo8ZQ5UqVe76+nmNjNSYSwJrHC88C/CViPyslNoNrFBKPQ2cA3o54v8IdAZOAbHAoCy3Op9Su3ZtunfvzhdffOFuU9zKP//8w9ixYzlz5gwAbdq0Yd68eXh5ebnZMo0md7JixQrGjx9/1+nNZjM//fTTbfuHRYQTJ05Q4c03AXgCeOaZZ3jqqafypevNtLitMIvIP0CdW4RfB9rcIlyAF7LEOk0qPD09efLJJ6lRowaVK1fOl7XDmJgY3nzzTZYvXw5A06ZNmTJlivbupdFkgujoaC5cuHD7iGkQFBSEr69vhuIGv/km1ZKS2Awc8/TkswEDKFiw4F1fOy+iR8nkMtq2bUvbtm3dbYZbEBHOnj3Lxo0bAbBYLLRt25Y6dW76btRoNHdAixYtmDFjxl2lVUrRsGHD205zEhHOnTlDk9WrsWFvYn3xxRf14N9boIVZk2v466+/6N+/P1euXMFisfDCCy/w8ssvu9ssjSbXU6dOHZd84J7u0YNKNhuLgBI1azJixIh87XozLbSvbE2uIDExkY0bN3L06FFEhMcee4zx48dTuHBhd5um0WQIpVS+XQ9cRJCoKFr8/Tfx2EdiV6hQQYtyGuTPX4kmV2EYBmvWrGHSpEkABAQE0LZtWwICAtxrmEaTipJMnXo/lStDgwbQuTM4vMQCULhwYd5999186fzm+vXrnGraFA9gAtC9d29mzZrlbrNyLLop282knPMrIogIhmFkaC5vfkBEWLhwIWPHjiU8PBwfHx+mTZtGv3793G2aRuPEXozXEBR0le3bywGwfz+EhkLVqvY4FouFmjVr0r59e0qXLp1vyreIsG7WLAYeOsR1YHG5cnw3ZgyVK1d2t2k5Fi3MbkJEuHr1KocOHeKTTz4B4M8//6R79+4UKlSIESNG4O/vT5kyZfK1J6uYmBi++uorLl++DEClSpXo1KlThuZLajSuYuvRVkASHTpcBuoBcKsu20qVKrFmzRqUUvmm5iwiNJo6FYV9uk7nhx8mKCgo33yY3A35943vJkSE2NhY/v77byZOnMjWrVuJjo4GICQkhLVr16KU4ttvv8XPz4+ZM2dSvHhxqlevTvHixQHXOPDICURHRzNnzhy2bt0KQLly5fjiiy8oUaKEmy3TaFJzJDgI2AtUSjeeyWTKV/PtRYR9H39MvZgYTgMbihTh3Vq18m1fe0bRwuxCRISvvvqKtWvXsm7dOhITExERzGYzzz33HKtWreL69evYbDYiIyOJjIykd+/eKKVo1aoVgYGB9OrVy+nhymQy5WmRPn36NO+99x5JSUlUq1aNTz/9lEaNGuXpe9Zo8hJbtmyh4ksvAfAk8Nlnn9G1a1f3GpUL0MLsAkSECxcusG/fPkaPHu2cyK+UokqVKrRu3Zq3336b/v37ExUVxZQpU7h06RIhISFcv34dwDl399dff6Vs2bIEBQUxePBgChQoQGBgYJ4Uq8qVKzNhwgTmz59Po0aNaNasWZ68T00uZulSiI+kXskD3M/jlNu6Bh5/3N1W5QgMw8BzxQruEWEnENCuHR06dMg3TfiZQWV2wYGsoGHDhrJnzx53m5HliAjR0dFs2bKF6dOns2PHDhISEgAoVaoUDz74IO+99x6VK1fGw8MDpRQiQkJCAoZhsGjRIn7++WdOnDjBsWOpFvRyNolVq1aNN954A29vb1q2bJnK+05eEDGr1UpiYiJmsxlPT888cU+ZQSm1V0QautuO2+D+l4orWLoUBg+G6bGIAU2G/MFAy2KGLHwQ+vblwAGIiIDmzd1tqOsREf45dYriNWpQ0GbjXg8P2g4axKeffupu01xFpl5UWpizCZvNxoIFC1i9ejWbNm3CarUCdreaY8aMoU2bNjz44IPpjr5OHqV97Ngxjhw5AsCiRYvYsGEDhmGQlJQE2AXYw8ODNm3a4OvrS+HChXn//ffx9/fXYpbH0MKcg6hUCc6dsy/po+DSG6V5hRnstTTGu2pFKlWCGTPg3nvda6Y7CAkJ4bdWrXjixAlWAL8+9xxvvfUWpUuXdrdprkILc07CMAz279/PL7/8wpQpU7h27RpgF+R69erx5JNPMmjQIPz8/O4q/wsXLnDt2jUOHjzIZ599RkREBAcPHkwVx2KxUL16dfz8/Bg9ejRFixalbt26GfZle6eICIcOHcrU6jQAFStWvK1bv/yOFuYcRPIH71NAGezr7GG/+aN//02NFMOy89PHsYiw9uuv6fTEEyggANi2Zw8NGjRws2UuJXP/8ORaWXob9me7CjgGHAUeAIoAG4CTjr+FHXEV8BH21aUOAPVvl3+DBg0kN2MYhlitVvnnn39k3rx5UqlSJTGZTAKIyWSSunXryiuvvCJhYWFis9my5Jo2m00SExPlyJEj8n//93/y5JNPir+/v/O6yZvFYhFfX1957733ZMGCBXL48GGxWq1ZZoeISFJSkrRu3VosFkumtp49e8qKFSskNjY2S+3LSwB7JANl1s1b3sZmE6NXLzHs05dvuSUoJVvq1ZMNP/wghmE4t7yOYRiSkJAgv1epIgIyEeT555+XkJAQd5vmajJVhjIqzIuAZxz7ng6h/gAY5QgbBUx27HcGfnIIdBNg1+3yd7cwJyQkSERExC23pKSk26aPi4uTl156SWrVqpVKFIsUKSLLli2TU6dOZVvBTM43MTFRtm/fLj/++KMEBQWJn5+fmM3mVPYAUq1aNWndurV89tlnEhERIdHR0Zm2KykpSVq2bHnTte5m8/b2lhYtWsjq1asz9OzzG1qY3UxEhBhVq4qAxHp4SMx/BDka5AeQuBQCvalWLXnvzTfFarXmeYG22WzywWuviRUkHKR2UJAEBwe72yx3kL3CDBQCzuBo9k4Rfhwo7dgvDRx37H8KPHGreGlt7hJmq9UqW7ZskbFjx0rlypVv2qpUqSJTpkyRXbt2pVmYrl+/LuPGjRNvb2+nuHh5eUm7du3kiy++kMTERJfek81mk/Pnz8vx48flpZdeki5dukjVqlVvEsDChQtL5cqVpV27drJ27Vr57bffxGq13tU1s1KYk7fSpUvL7t27s/jp5H60MLuR3bvFKFBABGSPr6+YQJ4AOQNiA4koXFiedPx+TSDTUgh0HMiykiXlqT59JCkpKc8KtNVqlT99fUVAXvL1lY0bN7rbJHeRqTKUkelS9wBXgS+UUnWwz6J/GSgpIiGOOJeBZG/kZYGUC3tedISFpAhDKTUYGAy4ZdkvwzDYuXMnffv2JTg4OPkj4iZGjBhB9erV+frrr7nvvvtumhgfERHBvn37SExMxMPDg/vvv5/27dszbNgwChYs6PK+JZPJRPny5RERpk+fDsCGDRv45ZdfOHjwIFu2bEFECAsLIywsjNOnT7Nx40ZKly7Niy++iLe3N7169aJo0aJ4eHjctSOA2rVrZ3h5ShHh22+/5fz589hsNgBCQ0P5/vvvCQoKwsvLK1/10WlyIHPnIkOHgmEwx9eXobGxAKz29OTYffcxbNgwunTpwtBjx+h26RLTp09nUXQ0w/fvZwowBOgTGspjy5ezavNmVt5/P6+8/joFChSgbt26ecbhhmnfPhrGxnLNx4d/WremWrVq7jYpd3I75QYaAlagseN4JvAuEP6feGGOv98DzVKEbwIapncNd9SYt2zZIuXKlXPW0AoWLCjFixdPtfn4+DjPV61aVY4cOXLLvC5fviyPPvqofP/993Lx4kUX30nGCQ0Nld27d8vq1aulXLlyUrRoUVFKpaqpKqWkZs2a0qhRI/n1118lNDRUoqKi0s33VjXm5557LsN2GYYhx44dk0GDBqXKw8fHR4YPH54naxZ3C7rG7HqefFIMkCSlpIfF4vx9+vn5Sbdu3eTkyZOpul1sNpvExMTI4cOHpVWrVtKwYcNb1qBnmc1S7Z57ZOPGjfLHH3/kjXEV1aqJARL/88/5faxIpspQRoS5FHA2xXFz4AdycVN2YmKivPHGG84CVrlyZfn666/l0qVLqba5c+dK6dKlnYOoPv7441v+0AzDkBs3buSaPtHExEQJCQmRI0eOyODBg6V///7yyCOP3NQnXaRIESlVqpQMGDBAwsLC0swvs8KczIoVK6R8+fKp8unevbsW5hRoYXYdRni4WB39yTcsFrknxe+yTp06MmTIEElMTEzz95k89uPkyZPSrl07adOmjTRp2PAmgZ6plNxTtqz8+OOPsmHDBtm+fbuzPzpX8c03dkmpV8/dluQEsleYxS6u24Bqjv3xwIeOLeXgrw8c+11IPfjrz9vl72phjoiIkMDAQAGkePHismLFilsWApvNJh999JF4eHgIIA888IDL+4yzk5SjRUNDQ+XDDz+Ud999VwIDA6VgwYLOEd4+Pj5y6NChNPPJKmG22WzSu3fvVPlUr15dDh8+nJnbzFNoYXYNxl9/iVGwoAjIDhBLit9kzZo15c8//7yrfE+fPi1dunSRVs2a3STQ0x3XKVGihKxZs0Y2b96c7QKd8h2QmU1ERIoXF1FK5MSJbLM3F5GpMpRRl5wvAkuVUp7AP8Ag7Gs5r1BKPQ2cA3o54v6IfWT2KSDWETfHICJcuXIFq9WKp6cnCxYsoGPHjrfswzSZTHTu3Jlp06Zx9uzZ5I+UPEPKey5RogTDhw/HMAz69etHXFwcI0aM4PDhw3h7e7tkNSelFMOGDeOHH35wLuxx/PhxTp06Rc2aNbP9+hoNAPPn2z16iTBdKYY5yr3FYmHKlCn07Nnzrh1lBAYG8v333xMbG8vevXv55swZrg4cyHMivAL8D/jkyhV6Pv44Fm9vGjRoQM2aNZk1a1a2LH6xatWqTK2LbDab+eKLL6i0ciVcvQpduuRPjypZTIaEWUT2Ye9r/i9tbhFXsK/ulSMREXbs2EGPHj1QSlG3bt10l1WsVKkSH3zwAb1793ahle7DZDJRqVIlRITly5djtVpRSlGwYMFsv7ZSiuLFi2tfuhq3kJSUhNG/P55ff40N6A18I0LBggWpVasWkyZNonbt2gQEBGT6Wr6+vjRv3py4hg3ZXakSay9cIPzZZxkYF/evQMfHM2LHDvbv38/Ro0cpVqwYc+bMwdfXl0KFCmXJgMjg4GC2bduWqTx6d+/O9oMHsZjN8OWXmfSsoYF8uIiFyWRiwIABGY5/5coVlixZkudqy7fDVWKcEhEhJiYGwzCcYZ6ennh6errUDk3uRES4ceOG0/1tSooUKZJuq49ERZFQuzYFz57lOnA/9jmiHh4ezJo1i2bNmlG5cuUsnx3g4+NDixYtSEhI4ETt2my5cIFzvXrxVEzMvwIdHc2I7dvBYuHUqVMUK1aMBQsWULBgQYoWLer2Ed3/99dfeACb77uPOiIUcas1eYN8J8x3gohw9OhRfvnlF3ebkm/48MMPnc3YAG3btqVly5ZutEiTG7h69SqHDx9mwoQJBAcHpzqnlGLUqFHUq1ePoKCgm9LK/v0YzZpRMDqaXUALIBGoVasWDRs2pGfPnhQoUCBb7ffy8qJWrVpUq1aN07t3szs0lMv9+/NocDCviNgF2mplxKFDYLHQqVMnAgICePvtt/H39+f+++9Pt+UvuwgAngZigMfOnGHQO+8wY8YMl9uR18gRwhwcHMy4ceMynU/RokUZMmRIls17NQyDqVOnEh8fj9ls5uGHH3b712leRUTYsGEDf/zxR6rWCR8fH7y9vd1omSanIyIsXLiQ119/Pc04Tz31FA888ADr169P7af+iy/gmWcwGQafeHgwJCkJb29vGtWuzccff0z9+vVdOofe09OTGjVqQI0ayPnzRN64wa/NmtH6+PHUAn38OFagY8eOBAQE8NVXX1GoUCGCgoLw9/e/6+sHBARQs2bNDHUnRUVFMXH/fiwiDAMiY2PZv38/0dHRLm9ty3NkdvRYVmxkkbeoggULSteuXWX16tUSEhKSqdGM8fHxMn36dClRooTTE9WBAwfuOr+8TFbMY/7rr78kKCgoVR6VKlWSrVu35r5pI9kIelR2Kmw2m0ybNk2KFSuWoXfEgAED5Pz58/bEgwaJgIjFIsayZTJ+/HgJDAyUuXPnSkRERI743SWPeo4OD5e/WraUJLP5plHcyffm6ekpTz31lIwfP16uXLmSofynT5+e6vm0adNGIiMjMzQS+/Jvv4kBcjmFLwSllPzvf/+Ty5cvZ/OTyfG4ZFR2riA6Opq1a9eyceNG7rvvPlauXHlXXsUSExNZsWIF48aNIyYmhsKFCzN37lz7l6wmQ1y+fJnt27enG+eee+7hwoULJCYmMmrUKA4dOpTqfL169WjcuLH2+qVJE8MwmD9/vnMVtzp16jBoUOqJIIcOHWL+/PmAfdnUFwYNonyXLnDwIBQuDL//jqpWjf+7cIGgoCC6desG5IwVoZJt8PX3p+7mzWC1cvHJJym1Zg2v2Gz2GjQwAvt7a/HixXh6enL69GkKFy5M+/bt6dKly11fNz1KvPgiAG+WLQsXLwL2it7ixYsZOnQoJUuWTC+5Jj0yq+xZsfGfr9oCBQpIkSJFMrwVKFDgll/HLVq0uOM5sLGxsTJt2jRnnoGBgbJ69eq79iOdH7hVjVkpddvVpJo1aya+vr5isVhSeSCzWCzyzDPPyIULF3JErSUnga4xp2LTpk3OsqqUkjVr1txUswsNDZV7771XAKkBEuPpaa8pN2ggEhfnzCs3+K9OttGWkCBX+/UTq4eHCEj8LWrQgNSqVUvCw8PTzC+tGvNt+flnERCjZk2ZPXu2eHl5OfPw9fVN1+9BPiFzmpjZDLJi+6+gzpw5Uy5cuJDhbf78+VK9enWnI5CU4rBw4cIMu4WLj4+XN998U3x9fZ3N1z/88EN+diuXIbJyEQsPDw/p2bOnREREuPu2ciRamFPz1VdfCdgXZRk7duwtRcUwDFm6dKk84+MjSWBfrnHIEFeamX0kJUnS0KFOgU4wmWRJ0aJSwPEuDAgISLdZ+66FuXRpu3wcPCh//PGH02ET2Je6HTFiRBbeZK4k7wnzl19+eUdPICkpSW7cuCFjx44VT0/PVHkFBQVJdHR0uukNw5Cff/5ZXnrpJacoly1b1ul5R5M+WSXMrVu3lqlTp0p4eHiOr7m4Cy3M/xIZGSnvv/++gN11a3ouccN797b7uwaZWKuWq0x0HUlJYrz8shiO1oBEk0mWlSwpHR56SG7cuJFmsrsS5jlz7NLRpo3j0kny1ltvpcrn8ccfz8q7y41kqgzliT5mi8VC4cKFGTlyJOfOnWPJkiXOc7GxsVy/fh1fX99b9pvYbDa+++47Ro4cyYkTJwCoWLEiM2bMoEWLFnoU9l1SoECBDDliCAwMZMiQIZhMJmrVqqU9fGkyzLlz59iyZQsPPPAANWrUuPV0odhYaNqUQvv2kVigALVjYrinbFlGud7c7MViQc2YAVOmIMOHY/nkE/qEhtI7PBzGj4cPP4Ss8AdgGDByJJhMsHw5YPf+5Y6pWnmZPPU0/fz8aNu2LT///LNzMMjZs2cZPnw4y5cvv0mYDcNg+/btvPDCC1y6dAmAsmXLMm3aNB599FEtypmgR48eTJ069bbxLBYL/v7+OWKgjSZ3cd999/HTTz+lHeHoUXjwQQgPx1arFrN79eL4G29wj+tMdD0pBJoRI1Bz58JHH8Gnn8Jzz2VeoIcPh+hoGDQIihXLMrM1qclzytOvXz/atWvnPDYMg6ioqJviiQjr16+nT58+XLp0CaUUJUuWZPHixTz22GNalDOJt7c3RYsWve2WVa4FNfkPpVSqLRXLlkGtWhAeDs8+y+ElS5j00UdusdMtWCwwfbpdRF95xR720Ufg7w8vvwyJiXeUndkMdWvbCJr+f/Q0rSZ22ifOc/v372flypXOYy8vL/r3758Vd5FvyXPqExYWlspzVFrs2rWLV155hcuXL1OoUCFGjhzJqlWrdPO1RpNJbty4kektLCwMm812dwa88AI8+aR9f9Ei5NNPmTlzJhEREYDdaU2+4TYCbbqF+9Jb4eMD+yr34BC18AyqyicL7LVuEeH8+fOcPHnSGddsNlO1atWsvpN8xW2bspVS1YCvUwQFAm8Cix3hlYCzQC8RCVP2T9eZ2FeYigUGishfWWv2rRGxewD68ccfnWEFChSgXr16qeJZrVa2bt3KyZMn8fT0ZMSIEYwYMcLpS1fsA9LSRNfwNJq06dChQ6bz8PDw4NNPP6VKlSoZF9L4eGjWDPbutQvPjh0QFERCfDx79+4l0VFLzJe1uWSB/vBDGDECHE3cz5vNGNjnQacr0WLA2rVQtCjNhwRx4ID9PRkTE8OsWbNISEhwRg0ICNB9zpnktk9PRI4DdQGUUmYgGFiDfQ3mTSIySSk1ynE8EugE3OvYGgNzHX+zFRHh4sWLrFy5MtWXdtmyZRk9erRTTEWEyMhIPv30U0QEk8nEnj17GDhwYIauM3DgQNq3b58dt6DR5An27NmT6TyUUvTu3Ztnn32Wbt26UbFixfQTnDwJTZrAjRv2Juw//gBfXxISEli1apVzYGe1atWoUqVKpu3LtfxXoGfN+nexDODnFAvIpCI+HkSwzvmUn76Cjh3tA2vnz5/Ptm3bnAvPKKX46KOPbv//0qTPnQzhBtoDOxz7x4HSjv3SwHHH/qfAEynSOOOlk2+mpkuJiBw7dkxatWolJpMpVV7t2rWT2NhYZzy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      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
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    },
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       "<path class=\"note\" d=\"M 331.167 82.1899 Q 331.946 82.1899, 332.561 82.5291 Q 333.177 82.8684, 333.516 83.4841 Q 333.855 84.0998, 333.855 84.8788 Q 333.855 85.7458, 333.466 86.4243 Q 333.089 87.0902, 332.41 87.4672 Q 331.732 87.8442, 330.865 87.8442 Q 329.282 87.8442, 328.478 86.7761 Q 327.686 85.6955, 327.686 83.5595 Q 327.686 81.1972, 328.666 79.9784 Q 329.659 78.747, 331.556 78.747 Q 332.109 78.747, 332.574 78.8727 Q 333.051 78.9983, 333.504 79.2622 L 333.014 80.1041 Q 332.36 79.7522, 331.569 79.7522 Q 330.312 79.7522, 329.671 80.569 Q 329.03 81.3731, 328.955 83.0443 Q 329.407 82.6296, 329.973 82.416 Q 330.551 82.1899, 331.167 82.1899 M 330.878 86.8138 Q 331.355 86.8138, 331.744 86.5625 Q 332.147 86.3112, 332.373 85.8714 Q 332.599 85.4317, 332.599 84.8914 Q 332.599 84.0998, 332.159 83.6474 Q 331.719 83.1951, 330.94 83.1951 Q 330.4 83.1951, 329.86 83.4212 Q 329.332 83.6349, 328.955 84.0118 Q 329.005 85.4819, 329.47 86.1479 Q 329.935 86.8138, 330.878 86.8138 \" fill=\"#000000\"/>\n",
       "<path class=\"note\" d=\"M 263.072 40.9074 L 258.411 40.9074 L 258.411 39.9022 L 264.316 39.9022 L 264.316 40.7943 L 260.723 48.7983 L 259.517 48.7983 L 263.072 40.9074 \" fill=\"#000000\"/>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from molscribe.chemistry import _postprocess_smiles, _convert_graph_to_smiles\n",
    "from molscribe.evaluate import canonicalize_smiles\n",
    "from molscribe.augment import CropWhite\n",
    "import albumentations as A\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "IPythonConsole.drawOptions.addAtomIndices = True\n",
    "IPythonConsole.molSize = 600,600\n",
    "\n",
    "transform = A.Compose([CropWhite(pad=0), A.Resize(384,384)])\n",
    "\n",
    "cnt = 0 \n",
    "for idx, row in data.iterrows():\n",
    "    gold_smiles,_ = canonicalize_smiles(row['SMILES'], ignore_cistrans=True, ignore_chiral=ignore_chiral)\n",
    "    pred_smiles = pred.loc[idx,'SMILES']\n",
    "    post_smiles,_ = canonicalize_smiles(pred.loc[idx,'post_SMILES'], ignore_cistrans=True, ignore_chiral=ignore_chiral)\n",
    "    graph_smiles,_ = canonicalize_smiles(pred.loc[idx,'graph_SMILES'], ignore_cistrans=True, ignore_chiral=ignore_chiral)\n",
    "    if gold_smiles == post_smiles:\n",
    "        continue\n",
    "    print('-' * 20)\n",
    "    cnt += 1\n",
    "    print(idx)\n",
    "    print(row['file_path'])\n",
    "    print('gold:', gold_smiles)\n",
    "    print('post:', post_smiles)\n",
    "    print('grph:', graph_smiles)\n",
    "    print('pred:', pred_smiles)\n",
    "    file = str(row['file_path'])\n",
    "    path = BASE + file\n",
    "    image = cv2.imread(path)\n",
    "    print(image.shape)\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(image)\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.axis('off')\n",
    "    img = transform(image=image)['image']\n",
    "    plt.imshow(img)\n",
    "    if 'node_coords' in pred.columns:\n",
    "        coords = np.array(eval(pred.loc[idx, 'node_coords']))\n",
    "        symbols = eval(pred.loc[idx, 'node_symbols'])\n",
    "        edges = eval(pred.loc[idx, 'edges'])\n",
    "        h, w, _ = img.shape\n",
    "        x, y = coords[:,0]*w, coords[:,1]*h\n",
    "        plt.scatter(x, y, color='r', marker='o')\n",
    "        for i in range(len(symbols)):\n",
    "            plt.text(x[i], y[i], symbols[i], color='blue')\n",
    "        for i in range(len(x)):\n",
    "            for j in range(len(x)):\n",
    "                if edges[i][j] != 0:\n",
    "                    if edges[i][j] in [5, 6]:\n",
    "                        color = 'blue' if edges[i][j] == 5 else 'green'\n",
    "                        plt.arrow(x[i], y[i], x[j]-x[i], y[j]-y[i], color=color, head_width=20)\n",
    "                    else:\n",
    "                        color = 'red' if edges[i][j] == 1 else 'orange'\n",
    "                        plt.plot([x[i], x[j]], [y[i], y[j]], color)\n",
    "    plt.show()\n",
    "    mol1 = Chem.MolFromSmiles(gold_smiles)\n",
    "    mol2 = Chem.MolFromSmiles(post_smiles)\n",
    "    svg = Draw.MolsToGridImage([mol1, mol2], subImgSize=(250,250), molsPerRow=2, useSVG=True)\n",
    "    display(svg)\n",
    "    if cnt == 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526f448a",
   "metadata": {},
   "source": [
    "## Analyze a single example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae9419fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real/acs/op300128c-Scheme-c9-1.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold: Cn1cc(C(=O)O[Na])cn1\n",
      "Pred: [NaO]C(=O)C1C=NN([Me])C=1\n",
      "Post: Cn1cc(C(=O)O[Na])cn1\n"
     ]
    },
    {
     "data": {
      "image/png": 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       "<IPython.core.display.SVG object>"
      ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[[0.206, 0.444], [0.381, 0.317], [0.381, 0.079], [0.556, 0.429], [0.73, 0.302], [0.873, 0.476], [0.778, 0.698], [0.873, 0.921], [0.587, 0.667]]\n",
      "['[NaO]', 'C', 'O', 'C', 'C', 'N', 'N', '[Me]', 'C']\n",
      "[[0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 2, 1, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 2], [0, 0, 0, 1, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 2, 0, 0, 1, 0, 0]]\n",
      "[NaO]C(=O)C1C=NN([Me])C=1\n",
      "post: Cn1cc(C(=O)O[Na])cn1\n",
      "graph: Cn1cc(C(=O)O[Na])cn1\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw\n",
    "IPythonConsole.drawOptions.addAtomIndices = True\n",
    "IPythonConsole.molSize = 300,300\n",
    "from molscribe.chemistry import _postprocess_smiles, _replace_functional_group, _convert_graph_to_smiles\n",
    "from molscribe.evaluate import canonicalize_smiles\n",
    "from molscribe.augment import CropWhite\n",
    "import albumentations as A\n",
    "\n",
    "i = 136\n",
    "gold_smiles = data.loc[i, 'SMILES']\n",
    "if gold_smiles is None or type(gold_smiles) is not str:\n",
    "    gold_smiles = ''\n",
    "print(data.loc[i, 'file_path'])\n",
    "path = BASE + data.loc[i, 'file_path']\n",
    "img = cv2.imread(path)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "transform = A.Compose([CropWhite(pad=0), A.Resize(384,384)])\n",
    "img = transform(image=img)['image']\n",
    "\n",
    "pred_row = pred.loc[i]\n",
    "pred_smiles = pred_row['SMILES']\n",
    "post_smiles = pred_row['post_SMILES']\n",
    "\n",
    "print('Gold:', canonicalize_smiles(gold_smiles)[0])\n",
    "print('Pred:', pred_smiles)\n",
    "print('Post:', post_smiles)\n",
    "\n",
    "coords = eval(pred_row['node_coords'])\n",
    "symbols = eval(pred_row['node_symbols'])\n",
    "edges = eval(pred_row['edges'])\n",
    "\n",
    "plt.imshow(img)\n",
    "h,w,_ = img.shape\n",
    "x, y = np.array(coords)[:,0]*w, np.array(coords)[:,1]*h\n",
    "plt.scatter(x, y, color='r', marker='o')\n",
    "for i in range(len(symbols)):\n",
    "    plt.text(x[i], y[i], symbols[i], color='blue')\n",
    "for i in range(len(x)):\n",
    "    for j in range(len(x)):\n",
    "        if edges[i][j] != 0:\n",
    "            if edges[i][j] in [5, 6]:\n",
    "                color = 'blue' if edges[i][j] == 5 else 'green'\n",
    "                plt.arrow(x[i], y[i], x[j]-x[i], y[j]-y[i], color=color, head_width=20)\n",
    "            else:\n",
    "                if edges[i][j] == 1:\n",
    "                    color = 'red'\n",
    "                elif edges[i][j] == 2:\n",
    "                    color = 'orange'\n",
    "                else:\n",
    "                    color = 'yellow'\n",
    "                plt.plot([x[i], x[j]], [y[i], y[j]], color)\n",
    "plt.show()\n",
    "\n",
    "mol_gold = Chem.MolFromSmiles(gold_smiles)\n",
    "pred_smiles = pred_smiles.replace('@', '')\n",
    "pred_smiles, mappings = _replace_functional_group(pred_smiles)\n",
    "mol_pred = Chem.MolFromSmiles(pred_smiles)\n",
    "mol_post = Chem.MolFromSmiles(post_smiles)\n",
    "img = Chem.Draw.MolsToGridImage([mol_gold,mol_pred,mol_post], subImgSize=(250,250), molsPerRow=3, useSVG=True)\n",
    "display(img)\n",
    "if mol_pred is not None:\n",
    "    chiral_centers = Chem.FindMolChiralCenters(mol_pred, includeUnassigned=True, includeCIP=False, useLegacyImplementation=False)\n",
    "    print(chiral_centers)\n",
    "\n",
    "print(coords)\n",
    "print(symbols)\n",
    "print(edges)\n",
    "\n",
    "pred_smiles = pred_row['SMILES']\n",
    "print(pred_smiles, flush=True)\n",
    "smiles, *_ = _postprocess_smiles(pred_smiles, coords, symbols, edges, molblock=True, debug=True)\n",
    "print('post:', smiles)\n",
    "\n",
    "smiles, *_ = _convert_graph_to_smiles(coords, symbols, edges, debug=True)\n",
    "print('graph:', smiles)"
   ]
  }
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